Fri Oct 18 08:57:52 UTC 2024  I: starting to build octave-statistics/trixie/armhf on jenkins on '2024-10-18 08:57'
Fri Oct 18 08:57:52 UTC 2024  I: The jenkins build log is/was available at https://jenkins.debian.net/userContent/reproducible/debian/build_service/armhf_8/10670/console.log
Fri Oct 18 08:57:52 UTC 2024  I: Downloading source for trixie/octave-statistics=1.7.0-2
--2024-10-18 08:57:52--  http://deb.debian.org/debian/pool/main/o/octave-statistics/octave-statistics_1.7.0-2.dsc
Connecting to 46.16.76.132:3128... connected.
Proxy request sent, awaiting response... 200 OK
Length: 2285 (2.2K) [text/prs.lines.tag]
Saving to: ‘octave-statistics_1.7.0-2.dsc’

     0K ..                                                    100%  315M=0s

2024-10-18 08:57:52 (315 MB/s) - ‘octave-statistics_1.7.0-2.dsc’ saved [2285/2285]

Fri Oct 18 08:57:52 UTC 2024  I: octave-statistics_1.7.0-2.dsc
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512

Format: 3.0 (quilt)
Source: octave-statistics
Binary: octave-statistics, octave-statistics-common
Architecture: any all
Version: 1.7.0-2
Maintainer: Debian Octave Group <team+pkg-octave-team@tracker.debian.org>
Uploaders: Sébastien Villemot <sebastien@debian.org>, Rafael Laboissière <rafael@debian.org>
Homepage: https://gnu-octave.github.io/packages/statistics/
Standards-Version: 4.7.0
Vcs-Browser: https://salsa.debian.org/pkg-octave-team/octave-statistics
Vcs-Git: https://salsa.debian.org/pkg-octave-team/octave-statistics.git
Testsuite: autopkgtest-pkg-octave
Build-Depends: debhelper-compat (= 13), dh-octave (>= 1.2.3), dh-sequence-octave, octave, octave-io
Build-Conflicts: octave-nan
Package-List:
 octave-statistics deb math optional arch=any
 octave-statistics-common deb math optional arch=all
Checksums-Sha1:
 137d4a9c6f1c6350e179777120b2d1ee6321880f 1293187 octave-statistics_1.7.0.orig.tar.gz
 3086681c41c0311c3e1d666dc1af9271e413965f 9464 octave-statistics_1.7.0-2.debian.tar.xz
Checksums-Sha256:
 1b074d671e7bb0fce41273b999dda1270596272cb5a168d4039462c7830d2d67 1293187 octave-statistics_1.7.0.orig.tar.gz
 9984d7f66c42b64706ada721794882f5fddda2092360df3fd4f0c8fe57d06436 9464 octave-statistics_1.7.0-2.debian.tar.xz
Files:
 12c3524034d747d85b9072675a74c659 1293187 octave-statistics_1.7.0.orig.tar.gz
 917104f2e6584f9206f318543ddd79a1 9464 octave-statistics_1.7.0-2.debian.tar.xz

-----BEGIN PGP SIGNATURE-----
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=qWdx
-----END PGP SIGNATURE-----
Fri Oct 18 08:57:52 UTC 2024  I: Checking whether the package is not for us
Fri Oct 18 08:57:52 UTC 2024  I: Starting 1st build on remote node virt64a-armhf-rb.debian.net.
Fri Oct 18 08:57:52 UTC 2024  I: Preparing to do remote build '1' on virt64a-armhf-rb.debian.net.
Fri Oct 18 10:06:55 UTC 2024  I: Deleting $TMPDIR on virt64a-armhf-rb.debian.net.
I: pbuilder: network access will be disabled during build
I: Current time: Thu Oct 17 20:57:59 -12 2024
I: pbuilder-time-stamp: 1729241879
I: Building the build Environment
I: extracting base tarball [/var/cache/pbuilder/trixie-reproducible-base.tgz]
I: copying local configuration
W: --override-config is not set; not updating apt.conf Read the manpage for details.
I: mounting /proc filesystem
I: mounting /sys filesystem
I: creating /{dev,run}/shm
I: mounting /dev/pts filesystem
I: redirecting /dev/ptmx to /dev/pts/ptmx
I: policy-rc.d already exists
I: Copying source file
I: copying [octave-statistics_1.7.0-2.dsc]
I: copying [./octave-statistics_1.7.0.orig.tar.gz]
I: copying [./octave-statistics_1.7.0-2.debian.tar.xz]
I: Extracting source
gpgv: Signature made Mon Sep 16 17:05:21 2024 
gpgv:                using RSA key 53951D95272E0C5B82BE8C4A2CECE9350ECEBE4A
gpgv: Can't check signature: No public key
dpkg-source: warning: cannot verify inline signature for ./octave-statistics_1.7.0-2.dsc: no acceptable signature found
dpkg-source: info: extracting octave-statistics in octave-statistics-1.7.0
dpkg-source: info: unpacking octave-statistics_1.7.0.orig.tar.gz
dpkg-source: info: unpacking octave-statistics_1.7.0-2.debian.tar.xz
dpkg-source: info: using patch list from debian/patches/series
dpkg-source: info: applying test-ClassificationPartitionedModel.patch
I: Not using root during the build.
I: Installing the build-deps
I: user script /srv/workspace/pbuilder/30583/tmp/hooks/D02_print_environment starting
I: set
  BUILDDIR='/build/reproducible-path'
  BUILDUSERGECOS='first user,first room,first work-phone,first home-phone,first other'
  BUILDUSERNAME='pbuilder1'
  BUILD_ARCH='armhf'
  DEBIAN_FRONTEND='noninteractive'
  DEB_BUILD_OPTIONS='buildinfo=+all reproducible=+all parallel=3 '
  DISTRIBUTION='trixie'
  HOME='/root'
  HOST_ARCH='armhf'
  IFS=' 	
  '
  INVOCATION_ID='71411321e49f468ba476a93f51cd3cd0'
  LANG='C'
  LANGUAGE='en_US:en'
  LC_ALL='C'
  MAIL='/var/mail/root'
  OPTIND='1'
  PATH='/usr/sbin:/usr/bin:/sbin:/bin:/usr/games'
  PBCURRENTCOMMANDLINEOPERATION='build'
  PBUILDER_OPERATION='build'
  PBUILDER_PKGDATADIR='/usr/share/pbuilder'
  PBUILDER_PKGLIBDIR='/usr/lib/pbuilder'
  PBUILDER_SYSCONFDIR='/etc'
  PPID='30583'
  PS1='# '
  PS2='> '
  PS4='+ '
  PWD='/'
  SHELL='/bin/bash'
  SHLVL='2'
  SUDO_COMMAND='/usr/bin/timeout -k 18.1h 18h /usr/bin/ionice -c 3 /usr/bin/nice /usr/sbin/pbuilder --build --configfile /srv/reproducible-results/rbuild-debian/r-b-build.KZf1uxCT/pbuilderrc_Aw4Y --distribution trixie --hookdir /etc/pbuilder/first-build-hooks --debbuildopts -b --basetgz /var/cache/pbuilder/trixie-reproducible-base.tgz --buildresult /srv/reproducible-results/rbuild-debian/r-b-build.KZf1uxCT/b1 --logfile b1/build.log octave-statistics_1.7.0-2.dsc'
  SUDO_GID='114'
  SUDO_UID='108'
  SUDO_USER='jenkins'
  TERM='unknown'
  TZ='/usr/share/zoneinfo/Etc/GMT+12'
  USER='root'
  _='/usr/bin/systemd-run'
  http_proxy='http://10.0.0.15:3142/'
I: uname -a
  Linux virt64a 6.1.0-26-arm64 #1 SMP Debian 6.1.112-1 (2024-09-30) aarch64 GNU/Linux
I: ls -l /bin
  lrwxrwxrwx 1 root root 7 Aug  4 21:30 /bin -> usr/bin
I: user script /srv/workspace/pbuilder/30583/tmp/hooks/D02_print_environment finished
 -> Attempting to satisfy build-dependencies
 -> Creating pbuilder-satisfydepends-dummy package
Package: pbuilder-satisfydepends-dummy
Version: 0.invalid.0
Architecture: armhf
Maintainer: Debian Pbuilder Team <pbuilder-maint@lists.alioth.debian.org>
Description: Dummy package to satisfy dependencies with aptitude - created by pbuilder
 This package was created automatically by pbuilder to satisfy the
 build-dependencies of the package being currently built.
Depends: debhelper-compat (= 13), dh-octave (>= 1.2.3), dh-sequence-octave, octave, octave-io
Conflicts: octave-nan
dpkg-deb: building package 'pbuilder-satisfydepends-dummy' in '/tmp/satisfydepends-aptitude/pbuilder-satisfydepends-dummy.deb'.
Selecting previously unselected package pbuilder-satisfydepends-dummy.
(Reading database ... 19520 files and directories currently installed.)
Preparing to unpack .../pbuilder-satisfydepends-dummy.deb ...
Unpacking pbuilder-satisfydepends-dummy (0.invalid.0) ...
dpkg: pbuilder-satisfydepends-dummy: dependency problems, but configuring anyway as you requested:
 pbuilder-satisfydepends-dummy depends on debhelper-compat (= 13); however:
  Package debhelper-compat is not installed.
 pbuilder-satisfydepends-dummy depends on dh-octave (>= 1.2.3); however:
  Package dh-octave is not installed.
 pbuilder-satisfydepends-dummy depends on dh-sequence-octave; however:
  Package dh-sequence-octave is not installed.
 pbuilder-satisfydepends-dummy depends on octave; however:
  Package octave is not installed.
 pbuilder-satisfydepends-dummy depends on octave-io; however:
  Package octave-io is not installed.

Setting up pbuilder-satisfydepends-dummy (0.invalid.0) ...
Reading package lists...
Building dependency tree...
Reading state information...
Initializing package states...
Writing extended state information...
Building tag database...
pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0)
pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0)
The following NEW packages will be installed:
  aglfn{a} appstream{a} autoconf{a} automake{a} autopoint{a} autotools-dev{a} bsdextrautils{a} ca-certificates{a} cme{a} debhelper{a} dh-autoreconf{a} dh-octave{a} dh-octave-autopkgtest{a} dh-strip-nondeterminism{a} diffstat{a} dwz{a} file{a} fontconfig{a} fontconfig-config{a} fonts-dejavu-core{a} fonts-dejavu-mono{a} fonts-freefont-otf{a} gettext{a} gettext-base{a} gfortran{a} gfortran-14{a} gfortran-14-arm-linux-gnueabihf{a} gfortran-arm-linux-gnueabihf{a} gnuplot-data{a} gnuplot-nox{a} gpg{a} gpgconf{a} groff-base{a} hdf5-helpers{a} hwloc-nox{a} intltool-debian{a} iso-codes{a} libabsl20230802{a} libaec-dev{a} libaec0{a} libalgorithm-c3-perl{a} libaliased-perl{a} libamd3{a} libaom3{a} libapp-cmd-perl{a} libappstream5{a} libapt-pkg-perl{a} libarchive-zip-perl{a} libarpack2t64{a} libarray-intspan-perl{a} libasound2-data{a} libasound2t64{a} libassuan9{a} libavahi-client3{a} libavahi-common-data{a} libavahi-common3{a} libavif16{a} libb-hooks-endofscope-perl{a} libb-hooks-op-check-perl{a} libb2-1{a} libberkeleydb-perl{a} libblas-dev{a} libblas3{a} libboolean-perl{a} libbrotli-dev{a} libbrotli1{a} libbtf2{a} libcairo2{a} libcamd3{a} libcapture-tiny-perl{a} libcarp-assert-more-perl{a} libccolamd3{a} libcgi-pm-perl{a} libcholmod5{a} libclass-c3-perl{a} libclass-data-inheritable-perl{a} libclass-inspector-perl{a} libclass-load-perl{a} libclass-method-modifiers-perl{a} libclass-xsaccessor-perl{a} libclone-choose-perl{a} libclone-perl{a} libcolamd3{a} libcom-err2{a} libcombblas2.0.0t64{a} libconfig-model-backend-yaml-perl{a} libconfig-model-dpkg-perl{a} libconfig-model-perl{a} libconfig-tiny-perl{a} libconst-fast-perl{a} libconvert-binhex-perl{a} libcpanel-json-xs-perl{a} libcups2t64{a} libcurl3t64-gnutls{a} libcurl4-openssl-dev{a} libcurl4t64{a} libcxsparse4{a} libdata-dpath-perl{a} libdata-messagepack-perl{a} libdata-optlist-perl{a} libdata-section-perl{a} libdata-validate-domain-perl{a} libdata-validate-ip-perl{a} libdata-validate-uri-perl{a} libdatrie1{a} libdav1d7{a} libdbus-1-3{a} libde265-0{a} libdebhelper-perl{a} libdeflate0{a} libdevel-callchecker-perl{a} libdevel-size-perl{a} libdevel-stacktrace-perl{a} libdouble-conversion3{a} libdrm-amdgpu1{a} libdrm-common{a} libdrm-radeon1{a} libdrm2{a} libduktape207{a} libdynaloader-functions-perl{a} libedit2{a} libegl-mesa0{a} libegl1{a} libelf1t64{a} libemail-address-xs-perl{a} libencode-locale-perl{a} liberror-perl{a} libevdev2{a} libevent-2.1-7t64{a} libexception-class-perl{a} libexpat1{a} libexporter-lite-perl{a} libexporter-tiny-perl{a} libfeature-compat-class-perl{a} libfeature-compat-try-perl{a} libfftw3-bin{a} libfftw3-dev{a} libfftw3-double3{a} libfftw3-mpi3{a} libfftw3-single3{a} libfile-basedir-perl{a} libfile-find-rule-perl{a} libfile-homedir-perl{a} libfile-listing-perl{a} libfile-sharedir-perl{a} libfile-stripnondeterminism-perl{a} libfile-which-perl{a} libflac12t64{a} libfltk-gl1.3t64{a} libfltk1.3t64{a} libfont-ttf-perl{a} libfontconfig1{a} libfreetype6{a} libfribidi0{a} libgav1-1{a} libgbm1{a} libgd3{a} libgetopt-long-descriptive-perl{a} libgfortran-14-dev{a} libgfortran5{a} libgif7{a} libgl-dev{a} libgl1{a} libgl1-mesa-dri{a} libgl2ps1.4{a} libglapi-mesa{a} libglib2.0-0t64{a} libglpk40{a} libglu1-mesa{a} libglvnd0{a} libglx-dev{a} libglx-mesa0{a} libglx0{a} libgmp-dev{a} libgmpxx4ldbl{a} libgnutls-dane0t64{a} libgnutls-openssl27t64{a} libgnutls28-dev{a} libgraphicsmagick++-q16-12t64{a} libgraphicsmagick-q16-3t64{a} libgraphite2-3{a} libgssapi-krb5-2{a} libgudev-1.0-0{a} libharfbuzz0b{a} libhash-merge-perl{a} libhdf5-103-1t64{a} libhdf5-cpp-103-1t64{a} libhdf5-dev{a} libhdf5-fortran-102t64{a} libhdf5-hl-100t64{a} libhdf5-hl-cpp-100t64{a} libhdf5-hl-fortran-100t64{a} libhdf5-mpich-103-1t64{a} libheif-plugin-dav1d{a} libheif-plugin-libde265{a} libheif1{a} libhtml-form-perl{a} libhtml-html5-entities-perl{a} libhtml-parser-perl{a} libhtml-tagset-perl{a} libhtml-tokeparser-simple-perl{a} libhtml-tree-perl{a} libhttp-cookies-perl{a} libhttp-date-perl{a} libhttp-message-perl{a} libhttp-negotiate-perl{a} libhwloc15{a} libhwy1t64{a} libhypre-2.29.0{a} libice6{a} libicu72{a} libidn2-dev{a} libimagequant0{a} libimath-3-1-29t64{a} libimport-into-perl{a} libindirect-perl{a} libinput-bin{a} libinput10{a} libio-html-perl{a} libio-interactive-perl{a} libio-socket-ssl-perl{a} libio-string-perl{a} libio-stringy-perl{a} libio-tiecombine-perl{a} libipc-run3-perl{a} libipc-system-simple-perl{a} libiterator-perl{a} libiterator-util-perl{a} libjack-jackd2-0{a} libjbig0{a} libjpeg-dev{a} libjpeg62-turbo{a} libjpeg62-turbo-dev{a} libjson-maybexs-perl{a} libjson-perl{a} libjxl0.9{a} libk5crypto3{a} libkeyutils1{a} libklu2{a} libkrb5-3{a} libkrb5support0{a} liblapack-dev{a} liblapack3{a} liblcms2-2{a} libldap-2.5-0{a} liblerc4{a} liblist-compare-perl{a} liblist-moreutils-perl{a} liblist-moreutils-xs-perl{a} liblist-someutils-perl{a} liblist-utilsby-perl{a} libllvm19{a} liblog-any-adapter-screen-perl{a} liblog-any-perl{a} liblog-log4perl-perl{a} libltdl7{a} liblua5.4-0{a} liblwp-mediatypes-perl{a} liblwp-protocol-https-perl{a} liblz1{a} liblzo2-2{a} libmagic-mgc{a} libmagic1t64{a} libmailtools-perl{a} libmarkdown2{a} libmd4c0{a} libmetis5{a} libmime-tools-perl{a} libmldbm-perl{a} libmodule-implementation-perl{a} libmodule-pluggable-perl{a} libmodule-runtime-perl{a} libmoo-perl{a} libmoox-aliases-perl{a} libmouse-perl{a} libmousex-nativetraits-perl{a} libmousex-strictconstructor-perl{a} libmp3lame0{a} libmpg123-0t64{a} libmpich12{a} libmro-compat-perl{a} libmtdev1t64{a} libmumps-5.6t64{a} libnamespace-clean-perl{a} libncurses-dev{a} libncurses6{a} libnet-domain-tld-perl{a} libnet-http-perl{a} libnet-ipv6addr-perl{a} libnet-netmask-perl{a} libnet-smtp-ssl-perl{a} libnet-ssleay-perl{a} libnetaddr-ip-perl{a} libnghttp2-14{a} libnghttp2-dev{a} libnghttp3-9{a} libngtcp2-16{a} libngtcp2-crypto-gnutls8{a} libnumber-compare-perl{a} libobject-pad-perl{a} libogg0{a} libopenexr-3-1-30{a} libopengl0{a} libopus0{a} libp11-kit-dev{a} libpackage-stash-perl{a} libpango-1.0-0{a} libpangocairo-1.0-0{a} libpangoft2-1.0-0{a} libparams-classify-perl{a} libparams-util-perl{a} libparams-validate-perl{a} libparse-debcontrol-perl{a} libparse-recdescent-perl{a} libpath-iterator-rule-perl{a} libpath-tiny-perl{a} libpcre2-16-0{a} libperlio-gzip-perl{a} libperlio-utf8-strict-perl{a} libpetsc-real3.20{a} libpipeline1{a} libpixman-1-0{a} libpkgconf3{a} libpng16-16t64{a} libpod-constants-perl{a} libpod-parser-perl{a} libpod-pom-perl{a} libportaudio2{a} libproc-processtable-perl{a} libproxy1v5{a} libpsl-dev{a} libpsl5t64{a} libptscotch-7.0{a} libqhull-r8.0{a} libqrupdate1{a} libqscintilla2-qt6-15{a} libqscintilla2-qt6-l10n{a} libqt6core5compat6{a} libqt6core6t64{a} libqt6dbus6{a} libqt6gui6{a} libqt6help6{a} libqt6network6{a} libqt6opengl6{a} libqt6openglwidgets6{a} libqt6printsupport6{a} libqt6sql6{a} libqt6widgets6{a} libqt6xml6{a} libraqm0{a} librav1e0.7{a} libreadline-dev{a} libreadline8t64{a} libregexp-common-perl{a} libregexp-pattern-license-perl{a} libregexp-pattern-perl{a} libregexp-wildcards-perl{a} librole-tiny-perl{a} librtmp-dev{a} librtmp1{a} libsamplerate0{a} libsasl2-2{a} libsasl2-modules-db{a} libscalapack-mpich2.2{a} libsensors-config{a} libsensors5{a} libsereal-decoder-perl{a} libsereal-encoder-perl{a} libset-intspan-perl{a} libsharpyuv0{a} libslurm41t64{a} libsm6{a} libsndfile1{a} libsoftware-copyright-perl{a} libsoftware-license-perl{a} libsoftware-licensemoreutils-perl{a} libsort-versions-perl{a} libspqr4{a} libssh2-1-dev{a} libssh2-1t64{a} libssl-dev{a} libstemmer0d{a} libstrictures-perl{a} libstring-copyright-perl{a} libstring-escape-perl{a} libstring-license-perl{a} libstring-rewriteprefix-perl{a} libsub-exporter-perl{a} libsub-exporter-progressive-perl{a} libsub-identify-perl{a} libsub-install-perl{a} libsub-name-perl{a} libsub-quote-perl{a} libsub-uplevel-perl{a} libsuitesparseconfig7{a} libsundials-ida6{a} libsundials-nvecparallel-petsc6{a} libsundials-sunlinsol3{a} libsundials-sunmatrix4{a} libsuperlu-dist8{a} libsuperlu6{a} libsvtav1enc2{a} libsyntax-keyword-try-perl{a} libsz2{a} libtasn1-6-dev{a} libterm-readkey-perl{a} libtest-exception-perl{a} libtext-autoformat-perl{a} libtext-glob-perl{a} libtext-levenshtein-damerau-perl{a} libtext-levenshteinxs-perl{a} libtext-markdown-discount-perl{a} libtext-reform-perl{a} libtext-template-perl{a} libtext-unidecode-perl{a} libtext-xslate-perl{a} libthai-data{a} libthai0{a} libtiff6{a} libtime-duration-perl{a} libtime-moment-perl{a} libtimedate-perl{a} libtoml-tiny-perl{a} libtool{a} libtry-tiny-perl{a} libts0t64{a} libuchardet0{a} libumfpack6{a} libunbound8{a} libunicode-utf8-perl{a} liburi-perl{a} libvariable-magic-perl{a} libvorbis0a{a} libvorbisenc2{a} libvulkan1{a} libwacom-common{a} libwacom9{a} libwayland-client0{a} libwayland-server0{a} libwebp7{a} libwebpmux3{a} libwmflite-0.2-7{a} libwww-mechanize-perl{a} libwww-perl{a} libwww-robotrules-perl{a} libx11-6{a} libx11-data{a} libx11-dev{a} libx11-xcb1{a} libxau-dev{a} libxau6{a} libxcb-cursor0{a} libxcb-dri2-0{a} libxcb-dri3-0{a} libxcb-glx0{a} libxcb-icccm4{a} libxcb-image0{a} libxcb-keysyms1{a} libxcb-present0{a} libxcb-randr0{a} libxcb-render-util0{a} libxcb-render0{a} libxcb-shape0{a} libxcb-shm0{a} libxcb-sync1{a} libxcb-util1{a} libxcb-xfixes0{a} libxcb-xinput0{a} libxcb-xkb1{a} libxcb1{a} libxcb1-dev{a} libxcursor1{a} libxdmcp-dev{a} libxdmcp6{a} libxext6{a} libxfixes3{a} libxft2{a} libxinerama1{a} libxkbcommon-x11-0{a} libxkbcommon0{a} libxml-libxml-perl{a} libxml-namespacesupport-perl{a} libxml-sax-base-perl{a} libxml-sax-perl{a} libxml2{a} libxmlb2{a} libxpm4{a} libxrender1{a} libxs-parse-keyword-perl{a} libxs-parse-sublike-perl{a} libxshmfence1{a} libxxf86vm1{a} libyaml-0-2{a} libyaml-libyaml-perl{a} libyaml-pp-perl{a} libyaml-tiny-perl{a} libyuv0{a} libz3-4{a} libzstd-dev{a} licensecheck{a} lintian{a} lzop{a} m4{a} man-db{a} mesa-libgallium{a} mpi-default-bin{a} mpich{a} netbase{a} nettle-dev{a} ocl-icd-libopencl1{a} octave{a} octave-common{a} octave-dev{a} octave-io{a} openssl{a} patchutils{a} perl-openssl-defaults{a} pkgconf{a} pkgconf-bin{a} plzip{a} po-debconf{a} readline-common{a} sensible-utils{a} shared-mime-info{a} t1utils{a} tex-common{a} texinfo{a} texinfo-lib{a} ucf{a} unzip{a} x11-common{a} x11proto-dev{a} xkb-data{a} xorg-sgml-doctools{a} xtrans-dev{a} zlib1g-dev{a} 
The following packages are RECOMMENDED but will NOT be installed:
  alsa-topology-conf alsa-ucm-conf bash-completion curl dbus default-jre-headless epstool fonts-liberation fonts-urw-base35 ghostscript gnupg groff krb5-locales libapache-poi-java libarchive-cpio-perl libauthen-sasl-perl libblis4 libcgi-fast-perl libclass-c3-xs-perl libconfig-model-approx-perl libconfig-model-lcdproc-perl libconfig-model-openssh-perl libconfig-model-systemd-perl libconfig-model-tkui-perl libdata-dump-perl libfreezethaw-perl libfuse-perl libglib2.0-data libgpm2 libheif-plugin-aomenc libheif-plugin-x265 libhtml-format-perl libhttp-daemon-perl libhwloc-plugins libio-compress-brotli-perl libipc-shareable-perl libjexcelapi-java libjopendocument-java libjson-xs-perl libldap-common liblist-someutils-xs-perl liblog-dispatch-perl libltdl-dev libmail-sendmail-perl libmath-base85-perl libmpich-dev libopenblas0 libpackage-stash-xs-perl libqt6sql6-ibase libqt6sql6-mysql libqt6sql6-odbc libqt6sql6-psql libqt6sql6-sqlite libre-engine-re2-perl libreoffice-core libreoffice-java-common libsasl2-modules libsocket6-perl libtasn1-doc libtie-ixhash-perl libtypes-serialiser-perl libxerces2-java libxml-sax-expat-perl libxstring-perl lynx mesa-vulkan-drivers octave-doc pstoedit publicsuffix qt6-gtk-platformtheme qt6-qpa-plugins qt6-translations-l10n qt6-wayland wget xdg-user-dirs 
0 packages upgraded, 536 newly installed, 0 to remove and 0 not upgraded.
Need to get 189 MB of archives. After unpacking 631 MB will be used.
Writing extended state information...
Get: 1 http://deb.debian.org/debian trixie/main armhf netbase all 6.4 [12.8 kB]
Get: 2 http://deb.debian.org/debian trixie/main armhf readline-common all 8.2-5 [69.3 kB]
Get: 3 http://deb.debian.org/debian trixie/main armhf sensible-utils all 0.0.24 [24.8 kB]
Get: 4 http://deb.debian.org/debian trixie/main armhf openssl armhf 3.3.2-1 [1348 kB]
Get: 5 http://deb.debian.org/debian trixie/main armhf ca-certificates all 20240203 [158 kB]
Get: 6 http://deb.debian.org/debian trixie/main armhf libmagic-mgc armhf 1:5.45-3 [314 kB]
Get: 7 http://deb.debian.org/debian trixie/main armhf libmagic1t64 armhf 1:5.45-3 [98.1 kB]
Get: 8 http://deb.debian.org/debian trixie/main armhf file armhf 1:5.45-3 [42.0 kB]
Get: 9 http://deb.debian.org/debian trixie/main armhf gettext-base armhf 0.22.5-2 [195 kB]
Get: 10 http://deb.debian.org/debian trixie/main armhf libuchardet0 armhf 0.0.8-1+b1 [65.7 kB]
Get: 11 http://deb.debian.org/debian trixie/main armhf groff-base armhf 1.23.0-5 [1091 kB]
Get: 12 http://deb.debian.org/debian trixie/main armhf bsdextrautils armhf 2.40.2-9 [88.8 kB]
Get: 13 http://deb.debian.org/debian trixie/main armhf libpipeline1 armhf 1.5.8-1 [35.0 kB]
Get: 14 http://deb.debian.org/debian trixie/main armhf man-db armhf 2.13.0-1 [1382 kB]
Get: 15 http://deb.debian.org/debian trixie/main armhf ucf all 3.0043+nmu1 [55.2 kB]
Get: 16 http://deb.debian.org/debian trixie/main armhf aglfn all 1.7+git20191031.4036a9c-2 [30.5 kB]
Get: 17 http://deb.debian.org/debian trixie/main armhf libglib2.0-0t64 armhf 2.82.1-1 [1325 kB]
Get: 18 http://deb.debian.org/debian trixie/main armhf libicu72 armhf 72.1-5 [9075 kB]
Get: 19 http://deb.debian.org/debian trixie/main armhf libxml2 armhf 2.12.7+dfsg+really2.9.14-0.1 [604 kB]
Get: 20 http://deb.debian.org/debian trixie/main armhf shared-mime-info armhf 2.4-5+b1 [753 kB]
Get: 21 http://deb.debian.org/debian trixie/main armhf libbrotli1 armhf 1.1.0-2+b4 [293 kB]
Get: 22 http://deb.debian.org/debian trixie/main armhf libkrb5support0 armhf 1.21.3-3 [30.0 kB]
Get: 23 http://deb.debian.org/debian trixie/main armhf libcom-err2 armhf 1.47.1-1 [22.1 kB]
Get: 24 http://deb.debian.org/debian trixie/main armhf libk5crypto3 armhf 1.21.3-3 [75.8 kB]
Get: 25 http://deb.debian.org/debian trixie/main armhf libkeyutils1 armhf 1.6.3-3 [7908 B]
Get: 26 http://deb.debian.org/debian trixie/main armhf libkrb5-3 armhf 1.21.3-3 [283 kB]
Get: 27 http://deb.debian.org/debian trixie/main armhf libgssapi-krb5-2 armhf 1.21.3-3 [114 kB]
Get: 28 http://deb.debian.org/debian trixie/main armhf libsasl2-modules-db armhf 2.1.28+dfsg1-8 [18.2 kB]
Get: 29 http://deb.debian.org/debian trixie/main armhf libsasl2-2 armhf 2.1.28+dfsg1-8 [50.2 kB]
Get: 30 http://deb.debian.org/debian trixie/main armhf libldap-2.5-0 armhf 2.5.18+dfsg-3 [163 kB]
Get: 31 http://deb.debian.org/debian trixie/main armhf libnghttp2-14 armhf 1.63.0-1 [62.9 kB]
Get: 32 http://deb.debian.org/debian trixie/main armhf libnghttp3-9 armhf 1.4.0-1 [55.1 kB]
Get: 33 http://deb.debian.org/debian trixie/main armhf libngtcp2-16 armhf 1.6.0-1 [118 kB]
Get: 34 http://deb.debian.org/debian trixie/main armhf libngtcp2-crypto-gnutls8 armhf 1.6.0-1 [17.1 kB]
Get: 35 http://deb.debian.org/debian trixie/main armhf libpsl5t64 armhf 0.21.2-1.1 [55.6 kB]
Get: 36 http://deb.debian.org/debian trixie/main armhf librtmp1 armhf 2.4+20151223.gitfa8646d.1-2+b4 [53.2 kB]
Get: 37 http://deb.debian.org/debian trixie/main armhf libssh2-1t64 armhf 1.11.0-7 [199 kB]
Get: 38 http://deb.debian.org/debian trixie/main armhf libcurl3t64-gnutls armhf 8.10.1-2 [308 kB]
Get: 39 http://deb.debian.org/debian trixie/main armhf libstemmer0d armhf 2.2.0-4+b1 [102 kB]
Get: 40 http://deb.debian.org/debian trixie/main armhf libxmlb2 armhf 0.3.20-1 [53.1 kB]
Get: 41 http://deb.debian.org/debian trixie/main armhf libyaml-0-2 armhf 0.2.5-1+b1 [45.5 kB]
Get: 42 http://deb.debian.org/debian trixie/main armhf libappstream5 armhf 1.0.3-1 [190 kB]
Get: 43 http://deb.debian.org/debian trixie/main armhf appstream armhf 1.0.3-1 [465 kB]
Get: 44 http://deb.debian.org/debian trixie/main armhf m4 armhf 1.4.19-4 [264 kB]
Get: 45 http://deb.debian.org/debian trixie/main armhf autoconf all 2.72-3 [493 kB]
Get: 46 http://deb.debian.org/debian trixie/main armhf autotools-dev all 20220109.1 [51.6 kB]
Get: 47 http://deb.debian.org/debian trixie/main armhf automake all 1:1.16.5-1.3 [823 kB]
Get: 48 http://deb.debian.org/debian trixie/main armhf autopoint all 0.22.5-2 [723 kB]
Get: 49 http://deb.debian.org/debian trixie/main armhf libcapture-tiny-perl all 0.48-2 [24.6 kB]
Get: 50 http://deb.debian.org/debian trixie/main armhf libparams-util-perl armhf 1.102-3 [23.0 kB]
Get: 51 http://deb.debian.org/debian trixie/main armhf libsub-install-perl all 0.929-1 [10.5 kB]
Get: 52 http://deb.debian.org/debian trixie/main armhf libdata-optlist-perl all 0.114-1 [10.6 kB]
Get: 53 http://deb.debian.org/debian trixie/main armhf libb-hooks-op-check-perl armhf 0.22-3+b1 [10.2 kB]
Get: 54 http://deb.debian.org/debian trixie/main armhf libdynaloader-functions-perl all 0.004-1 [12.1 kB]
Get: 55 http://deb.debian.org/debian trixie/main armhf libdevel-callchecker-perl armhf 0.009-1 [15.7 kB]
Get: 56 http://deb.debian.org/debian trixie/main armhf libparams-classify-perl armhf 0.015-2+b3 [21.3 kB]
Get: 57 http://deb.debian.org/debian trixie/main armhf libmodule-runtime-perl all 0.016-2 [19.6 kB]
Get: 58 http://deb.debian.org/debian trixie/main armhf libtry-tiny-perl all 0.32-1 [22.9 kB]
Get: 59 http://deb.debian.org/debian trixie/main armhf libmodule-implementation-perl all 0.09-2 [12.6 kB]
Get: 60 http://deb.debian.org/debian trixie/main armhf libpackage-stash-perl all 0.40-1 [22.0 kB]
Get: 61 http://deb.debian.org/debian trixie/main armhf libclass-load-perl all 0.25-2 [15.3 kB]
Get: 62 http://deb.debian.org/debian trixie/main armhf libio-stringy-perl all 2.113-2 [48.3 kB]
Get: 63 http://deb.debian.org/debian trixie/main armhf libparams-validate-perl armhf 1.31-2+b2 [62.2 kB]
Get: 64 http://deb.debian.org/debian trixie/main armhf libsub-exporter-perl all 0.990-1 [50.6 kB]
Get: 65 http://deb.debian.org/debian trixie/main armhf libgetopt-long-descriptive-perl all 0.111-1 [27.8 kB]
Get: 66 http://deb.debian.org/debian trixie/main armhf libio-tiecombine-perl all 1.005-3 [10.8 kB]
Get: 67 http://deb.debian.org/debian trixie/main armhf libmodule-pluggable-perl all 5.2-5 [23.0 kB]
Get: 68 http://deb.debian.org/debian trixie/main armhf libstring-rewriteprefix-perl all 0.009-1 [7140 B]
Get: 69 http://deb.debian.org/debian trixie/main armhf libapp-cmd-perl all 0.336-1 [65.8 kB]
Get: 70 http://deb.debian.org/debian trixie/main armhf libboolean-perl all 0.46-3 [9924 B]
Get: 71 http://deb.debian.org/debian trixie/main armhf libsub-uplevel-perl all 0.2800-3 [14.0 kB]
Get: 72 http://deb.debian.org/debian trixie/main armhf libtest-exception-perl all 0.43-3 [16.9 kB]
Get: 73 http://deb.debian.org/debian trixie/main armhf libcarp-assert-more-perl all 2.4.0-1 [20.2 kB]
Get: 74 http://deb.debian.org/debian trixie/main armhf libfile-which-perl all 1.27-2 [15.1 kB]
Get: 75 http://deb.debian.org/debian trixie/main armhf libfile-homedir-perl all 1.006-2 [42.4 kB]
Get: 76 http://deb.debian.org/debian trixie/main armhf libclone-choose-perl all 0.010-2 [8676 B]
Get: 77 http://deb.debian.org/debian trixie/main armhf libhash-merge-perl all 0.302-1 [14.7 kB]
Get: 78 http://deb.debian.org/debian trixie/main armhf libjson-perl all 4.10000-1 [87.5 kB]
Get: 79 http://deb.debian.org/debian trixie/main armhf libexporter-tiny-perl all 1.006002-1 [38.7 kB]
Get: 80 http://deb.debian.org/debian trixie/main armhf liblist-moreutils-xs-perl armhf 0.430-4 [36.6 kB]
Get: 81 http://deb.debian.org/debian trixie/main armhf liblist-moreutils-perl all 0.430-2 [46.9 kB]
Get: 82 http://deb.debian.org/debian trixie/main armhf liblog-log4perl-perl all 1.57-1 [367 kB]
Get: 83 http://deb.debian.org/debian trixie/main armhf libmouse-perl armhf 2.5.11-1 [139 kB]
Get: 84 http://deb.debian.org/debian trixie/main armhf libmousex-nativetraits-perl all 1.09-3 [53.5 kB]
Get: 85 http://deb.debian.org/debian trixie/main armhf libmousex-strictconstructor-perl all 0.02-3 [5304 B]
Get: 86 http://deb.debian.org/debian trixie/main armhf libparse-recdescent-perl all 1.967015+dfsg-4 [147 kB]
Get: 87 http://deb.debian.org/debian trixie/main armhf libpath-tiny-perl all 0.146-1 [56.2 kB]
Get: 88 http://deb.debian.org/debian trixie/main armhf libpod-pom-perl all 2.01-4 [65.0 kB]
Get: 89 http://deb.debian.org/debian trixie/main armhf libregexp-common-perl all 2024080801-1 [167 kB]
Get: 90 http://deb.debian.org/debian trixie/main armhf libyaml-tiny-perl all 1.74-1 [30.7 kB]
Get: 91 http://deb.debian.org/debian trixie/main armhf libconfig-model-perl all 2.154-1 [372 kB]
Get: 92 http://deb.debian.org/debian trixie/main armhf libyaml-pp-perl all 0.38.0-1 [109 kB]
Get: 93 http://deb.debian.org/debian trixie/main armhf cme all 1.040-1 [68.8 kB]
Get: 94 http://deb.debian.org/debian trixie/main armhf libdebhelper-perl all 13.20 [89.7 kB]
Get: 95 http://deb.debian.org/debian trixie/main armhf libtool all 2.4.7-7 [517 kB]
Get: 96 http://deb.debian.org/debian trixie/main armhf dh-autoreconf all 20 [17.1 kB]
Get: 97 http://deb.debian.org/debian trixie/main armhf libarchive-zip-perl all 1.68-1 [104 kB]
Get: 98 http://deb.debian.org/debian trixie/main armhf libfile-stripnondeterminism-perl all 1.14.0-1 [19.5 kB]
Get: 99 http://deb.debian.org/debian trixie/main armhf dh-strip-nondeterminism all 1.14.0-1 [8448 B]
Get: 100 http://deb.debian.org/debian trixie/main armhf libelf1t64 armhf 0.191-2 [183 kB]
Get: 101 http://deb.debian.org/debian trixie/main armhf dwz armhf 0.15-1+b2 [106 kB]
Get: 102 http://deb.debian.org/debian trixie/main armhf gettext armhf 0.22.5-2 [1485 kB]
Get: 103 http://deb.debian.org/debian trixie/main armhf intltool-debian all 0.35.0+20060710.6 [22.9 kB]
Get: 104 http://deb.debian.org/debian trixie/main armhf po-debconf all 1.0.21+nmu1 [248 kB]
Get: 105 http://deb.debian.org/debian trixie/main armhf debhelper all 13.20 [915 kB]
Get: 106 http://deb.debian.org/debian trixie/main armhf gnuplot-data all 6.0.0+dfsg1-3 [72.3 kB]
Get: 107 http://deb.debian.org/debian trixie/main armhf libexpat1 armhf 2.6.3-1 [83.2 kB]
Get: 108 http://deb.debian.org/debian trixie/main armhf libpng16-16t64 armhf 1.6.44-2 [263 kB]
Get: 109 http://deb.debian.org/debian trixie/main armhf libfreetype6 armhf 2.13.3+dfsg-1 [385 kB]
Get: 110 http://deb.debian.org/debian trixie/main armhf fonts-dejavu-mono all 2.37-8 [489 kB]
Get: 111 http://deb.debian.org/debian trixie/main armhf fonts-dejavu-core all 2.37-8 [840 kB]
Get: 112 http://deb.debian.org/debian trixie/main armhf fonts-freefont-otf all 20211204+svn4273-2 [4328 kB]
Get: 113 http://deb.debian.org/debian trixie/main armhf fontconfig-config armhf 2.15.0-1.1 [317 kB]
Get: 114 http://deb.debian.org/debian trixie/main armhf libfontconfig1 armhf 2.15.0-1.1 [370 kB]
Get: 115 http://deb.debian.org/debian trixie/main armhf libpixman-1-0 armhf 0.42.2-1+b1 [476 kB]
Get: 116 http://deb.debian.org/debian trixie/main armhf libxau6 armhf 1:1.0.9-1+b1 [17.4 kB]
Get: 117 http://deb.debian.org/debian trixie/main armhf libxdmcp6 armhf 1:1.1.2-3+b1 [23.0 kB]
Get: 118 http://deb.debian.org/debian trixie/main armhf libxcb1 armhf 1.17.0-2 [140 kB]
Get: 119 http://deb.debian.org/debian trixie/main armhf libx11-data all 2:1.8.7-1 [328 kB]
Get: 120 http://deb.debian.org/debian trixie/main armhf libx11-6 armhf 2:1.8.7-1+b1 [739 kB]
Get: 121 http://deb.debian.org/debian trixie/main armhf libxcb-render0 armhf 1.17.0-2 [114 kB]
Get: 122 http://deb.debian.org/debian trixie/main armhf libxcb-shm0 armhf 1.17.0-2 [105 kB]
Get: 123 http://deb.debian.org/debian trixie/main armhf libxext6 armhf 2:1.3.4-1+b2 [45.2 kB]
Get: 124 http://deb.debian.org/debian trixie/main armhf libxrender1 armhf 1:0.9.10-1.1+b1 [24.9 kB]
Get: 125 http://deb.debian.org/debian trixie/main armhf libcairo2 armhf 1.18.2-2 [443 kB]
Get: 126 http://deb.debian.org/debian trixie/main armhf libedit2 armhf 3.1-20240808-1 [77.9 kB]
Get: 127 http://deb.debian.org/debian trixie/main armhf libaom3 armhf 3.9.1-1 [1558 kB]
Get: 128 http://deb.debian.org/debian trixie/main armhf libdav1d7 armhf 1.4.3-1 [256 kB]
Get: 129 http://deb.debian.org/debian trixie/main armhf libabsl20230802 armhf 20230802.1-4 [432 kB]
Get: 130 http://deb.debian.org/debian trixie/main armhf libgav1-1 armhf 0.19.0-2+b1 [299 kB]
Get: 131 http://deb.debian.org/debian trixie/main armhf librav1e0.7 armhf 0.7.1-7+b1 [563 kB]
Get: 132 http://deb.debian.org/debian trixie/main armhf libsvtav1enc2 armhf 2.2.1+dfsg-2 [937 kB]
Get: 133 http://deb.debian.org/debian trixie/main armhf libjpeg62-turbo armhf 1:2.1.5-3 [143 kB]
Get: 134 http://deb.debian.org/debian trixie/main armhf libyuv0 armhf 0.0.1888.20240710-3 [85.8 kB]
Get: 135 http://deb.debian.org/debian trixie/main armhf libavif16 armhf 1.1.1-1 [103 kB]
Get: 136 http://deb.debian.org/debian trixie/main armhf libsharpyuv0 armhf 1.4.0-0.1 [111 kB]
Get: 137 http://deb.debian.org/debian trixie/main armhf libheif-plugin-dav1d armhf 1.18.2-2 [10.5 kB]
Get: 138 http://deb.debian.org/debian trixie/main armhf libde265-0 armhf 1.0.15-1+b2 [145 kB]
Get: 139 http://deb.debian.org/debian trixie/main armhf libheif-plugin-libde265 armhf 1.18.2-2 [13.3 kB]
Get: 140 http://deb.debian.org/debian trixie/main armhf libheif1 armhf 1.18.2-2 [262 kB]
Get: 141 http://deb.debian.org/debian trixie/main armhf libimagequant0 armhf 2.18.0-1+b1 [30.7 kB]
Get: 142 http://deb.debian.org/debian trixie/main armhf libfribidi0 armhf 1.0.15-1 [70.0 kB]
Get: 143 http://deb.debian.org/debian trixie/main armhf libgraphite2-3 armhf 1.3.14-2 [63.2 kB]
Get: 144 http://deb.debian.org/debian trixie/main armhf libharfbuzz0b armhf 9.0.0-1 [417 kB]
Get: 145 http://deb.debian.org/debian trixie/main armhf libraqm0 armhf 0.10.1-1+b1 [11.8 kB]
Get: 146 http://deb.debian.org/debian trixie/main armhf libdeflate0 armhf 1.22-1 [36.3 kB]
Get: 147 http://deb.debian.org/debian trixie/main armhf libjbig0 armhf 2.1-6.1+b1 [27.3 kB]
Get: 148 http://deb.debian.org/debian trixie/main armhf liblerc4 armhf 4.0.0+ds-4+b1 [137 kB]
Get: 149 http://deb.debian.org/debian trixie/main armhf libwebp7 armhf 1.4.0-0.1 [265 kB]
Get: 150 http://deb.debian.org/debian trixie/main armhf libtiff6 armhf 4.5.1+git230720-5 [302 kB]
Get: 151 http://deb.debian.org/debian trixie/main armhf libxpm4 armhf 1:3.5.17-1+b1 [50.0 kB]
Get: 152 http://deb.debian.org/debian trixie/main armhf libgd3 armhf 2.3.3-12 [104 kB]
Get: 153 http://deb.debian.org/debian trixie/main armhf liblua5.4-0 armhf 5.4.6-3+b1 [122 kB]
Get: 154 http://deb.debian.org/debian trixie/main armhf fontconfig armhf 2.15.0-1.1 [461 kB]
Get: 155 http://deb.debian.org/debian trixie/main armhf libthai-data all 0.1.29-2 [168 kB]
Get: 156 http://deb.debian.org/debian trixie/main armhf libdatrie1 armhf 0.2.13-3 [34.4 kB]
Get: 157 http://deb.debian.org/debian trixie/main armhf libthai0 armhf 0.1.29-2 [45.8 kB]
Get: 158 http://deb.debian.org/debian trixie/main armhf libpango-1.0-0 armhf 1.54.0+ds-2 [198 kB]
Get: 159 http://deb.debian.org/debian trixie/main armhf libpangoft2-1.0-0 armhf 1.54.0+ds-2 [47.5 kB]
Get: 160 http://deb.debian.org/debian trixie/main armhf libpangocairo-1.0-0 armhf 1.54.0+ds-2 [31.1 kB]
Get: 161 http://deb.debian.org/debian trixie/main armhf libwebpmux3 armhf 1.4.0-0.1 [120 kB]
Get: 162 http://deb.debian.org/debian trixie/main armhf gnuplot-nox armhf 6.0.0+dfsg1-3 [838 kB]
Get: 163 http://deb.debian.org/debian trixie/main armhf dh-octave-autopkgtest all 1.8.0 [10.0 kB]
Get: 164 http://deb.debian.org/debian trixie/main armhf libapt-pkg-perl armhf 0.1.40+b5 [62.5 kB]
Get: 165 http://deb.debian.org/debian trixie/main armhf libarray-intspan-perl all 2.004-2 [25.7 kB]
Get: 166 http://deb.debian.org/debian trixie/main armhf libyaml-libyaml-perl armhf 0.902.0+ds-2 [33.1 kB]
Get: 167 http://deb.debian.org/debian trixie/main armhf libconfig-model-backend-yaml-perl all 2.134-2 [10.8 kB]
Get: 168 http://deb.debian.org/debian trixie/main armhf libexporter-lite-perl all 0.09-2 [10.7 kB]
Get: 169 http://deb.debian.org/debian trixie/main armhf libencode-locale-perl all 1.05-3 [12.9 kB]
Get: 170 http://deb.debian.org/debian trixie/main armhf libtimedate-perl all 2.3300-2 [39.3 kB]
Get: 171 http://deb.debian.org/debian trixie/main armhf libhttp-date-perl all 6.06-1 [10.7 kB]
Get: 172 http://deb.debian.org/debian trixie/main armhf libfile-listing-perl all 6.16-1 [12.4 kB]
Get: 173 http://deb.debian.org/debian trixie/main armhf libhtml-tagset-perl all 3.24-1 [14.7 kB]
Get: 174 http://deb.debian.org/debian trixie/main armhf liburi-perl all 5.30-1 [105 kB]
Get: 175 http://deb.debian.org/debian trixie/main armhf libhtml-parser-perl armhf 3.83-1 [96.1 kB]
Get: 176 http://deb.debian.org/debian trixie/main armhf libhtml-tree-perl all 5.07-3 [211 kB]
Get: 177 http://deb.debian.org/debian trixie/main armhf libclone-perl armhf 0.47-1 [13.0 kB]
Get: 178 http://deb.debian.org/debian trixie/main armhf libio-html-perl all 1.004-3 [16.2 kB]
Get: 179 http://deb.debian.org/debian trixie/main armhf liblwp-mediatypes-perl all 6.04-2 [20.2 kB]
Get: 180 http://deb.debian.org/debian trixie/main armhf libhttp-message-perl all 7.00-2 [79.8 kB]
Get: 181 http://deb.debian.org/debian trixie/main armhf libhttp-cookies-perl all 6.11-1 [19.1 kB]
Get: 182 http://deb.debian.org/debian trixie/main armhf libhttp-negotiate-perl all 6.01-2 [13.1 kB]
Get: 183 http://deb.debian.org/debian trixie/main armhf perl-openssl-defaults armhf 7+b2 [6708 B]
Get: 184 http://deb.debian.org/debian trixie/main armhf libnet-ssleay-perl armhf 1.94-1+b1 [319 kB]
Get: 185 http://deb.debian.org/debian trixie/main armhf libio-socket-ssl-perl all 2.089-1 [223 kB]
Get: 186 http://deb.debian.org/debian trixie/main armhf libnet-http-perl all 6.23-1 [23.9 kB]
Get: 187 http://deb.debian.org/debian trixie/main armhf liblwp-protocol-https-perl all 6.14-1 [10.8 kB]
Get: 188 http://deb.debian.org/debian trixie/main armhf libwww-robotrules-perl all 6.02-1 [12.9 kB]
Get: 189 http://deb.debian.org/debian trixie/main armhf libwww-perl all 6.77-1 [183 kB]
Get: 190 http://deb.debian.org/debian trixie/main armhf liberror-perl all 0.17029-2 [29.0 kB]
Get: 191 http://deb.debian.org/debian trixie/main armhf libparse-debcontrol-perl all 2.005-6 [21.6 kB]
Get: 192 http://deb.debian.org/debian trixie/main armhf libsoftware-copyright-perl all 0.012-2 [18.0 kB]
Get: 193 http://deb.debian.org/debian trixie/main armhf libalgorithm-c3-perl all 0.11-2 [10.8 kB]
Get: 194 http://deb.debian.org/debian trixie/main armhf libclass-c3-perl all 0.35-2 [21.0 kB]
Get: 195 http://deb.debian.org/debian trixie/main armhf libmro-compat-perl all 0.15-2 [11.8 kB]
Get: 196 http://deb.debian.org/debian trixie/main armhf libdata-section-perl all 0.200008-1 [13.1 kB]
Get: 197 http://deb.debian.org/debian trixie/main armhf libtext-template-perl all 1.61-1 [54.4 kB]
Get: 198 http://deb.debian.org/debian trixie/main armhf libsoftware-license-perl all 0.104006-1 [116 kB]
Get: 199 http://deb.debian.org/debian trixie/main armhf libsoftware-licensemoreutils-perl all 1.009-1 [22.0 kB]
Get: 200 http://deb.debian.org/debian trixie/main armhf libsort-versions-perl all 1.62-3 [8928 B]
Get: 201 http://deb.debian.org/debian trixie/main armhf libtext-reform-perl all 1.20-5 [36.0 kB]
Get: 202 http://deb.debian.org/debian trixie/main armhf libtext-autoformat-perl all 1.750000-2 [35.2 kB]
Get: 203 http://deb.debian.org/debian trixie/main armhf libtext-levenshtein-damerau-perl all 0.41-3 [12.3 kB]
Get: 204 http://deb.debian.org/debian trixie/main armhf libtoml-tiny-perl all 0.18-1 [23.0 kB]
Get: 205 http://deb.debian.org/debian trixie/main armhf libclass-inspector-perl all 1.36-3 [17.5 kB]
Get: 206 http://deb.debian.org/debian trixie/main armhf libfile-sharedir-perl all 1.118-3 [16.0 kB]
Get: 207 http://deb.debian.org/debian trixie/main armhf libindirect-perl armhf 0.39-2+b3 [25.8 kB]
Get: 208 http://deb.debian.org/debian trixie/main armhf libxs-parse-keyword-perl armhf 0.46-1 [62.0 kB]
Get: 209 http://deb.debian.org/debian trixie/main armhf libxs-parse-sublike-perl armhf 0.28-1 [44.1 kB]
Get: 210 http://deb.debian.org/debian trixie/main armhf libobject-pad-perl armhf 0.814-1 [114 kB]
Get: 211 http://deb.debian.org/debian trixie/main armhf libfeature-compat-class-perl all 0.07-1 [11.5 kB]
Get: 212 http://deb.debian.org/debian trixie/main armhf libsyntax-keyword-try-perl armhf 0.30-1 [26.2 kB]
Get: 213 http://deb.debian.org/debian trixie/main armhf libfeature-compat-try-perl all 0.05-1 [10.4 kB]
Get: 214 http://deb.debian.org/debian trixie/main armhf libio-interactive-perl all 1.025-1 [11.4 kB]
Get: 215 http://deb.debian.org/debian trixie/main armhf liblog-any-perl all 1.717-1 [78.9 kB]
Get: 216 http://deb.debian.org/debian trixie/main armhf liblog-any-adapter-screen-perl all 0.141-1 [14.0 kB]
Get: 217 http://deb.debian.org/debian trixie/main armhf libsub-exporter-progressive-perl all 0.001013-3 [7496 B]
Get: 218 http://deb.debian.org/debian trixie/main armhf libvariable-magic-perl armhf 0.64-1 [42.2 kB]
Get: 219 http://deb.debian.org/debian trixie/main armhf libb-hooks-endofscope-perl all 0.28-1 [17.5 kB]
Get: 220 http://deb.debian.org/debian trixie/main armhf libsub-identify-perl armhf 0.14-3+b2 [10.8 kB]
Get: 221 http://deb.debian.org/debian trixie/main armhf libsub-name-perl armhf 0.27-1+b2 [12.0 kB]
Get: 222 http://deb.debian.org/debian trixie/main armhf libnamespace-clean-perl all 0.27-2 [17.8 kB]
Get: 223 http://deb.debian.org/debian trixie/main armhf libnumber-compare-perl all 0.03-3 [6332 B]
Get: 224 http://deb.debian.org/debian trixie/main armhf libtext-glob-perl all 0.11-3 [7676 B]
Get: 225 http://deb.debian.org/debian trixie/main armhf libpath-iterator-rule-perl all 1.015-2 [41.7 kB]
Get: 226 http://deb.debian.org/debian trixie/main armhf libpod-parser-perl all 1.67-1 [94.1 kB]
Get: 227 http://deb.debian.org/debian trixie/main armhf libpod-constants-perl all 0.19-2 [17.3 kB]
Get: 228 http://deb.debian.org/debian trixie/main armhf libset-intspan-perl all 1.19-3 [25.3 kB]
Get: 229 http://deb.debian.org/debian trixie/main armhf libstring-copyright-perl all 0.003014-1 [23.4 kB]
Get: 230 http://deb.debian.org/debian trixie/main armhf libstring-escape-perl all 2010.002-3 [18.7 kB]
Get: 231 http://deb.debian.org/debian trixie/main armhf libregexp-pattern-license-perl all 3.11.2-1 [94.6 kB]
Get: 232 http://deb.debian.org/debian trixie/main armhf libregexp-pattern-perl all 0.2.14-2 [18.7 kB]
Get: 233 http://deb.debian.org/debian trixie/main armhf libstring-license-perl all 0.0.11-1 [34.7 kB]
Get: 234 http://deb.debian.org/debian trixie/main armhf licensecheck all 3.3.9-1 [50.1 kB]
Get: 235 http://deb.debian.org/debian trixie/main armhf diffstat armhf 1.66-1 [32.0 kB]
Get: 236 http://deb.debian.org/debian trixie/main armhf libassuan9 armhf 3.0.1-2 [53.7 kB]
Get: 237 http://deb.debian.org/debian trixie/main armhf libreadline8t64 armhf 8.2-5 [146 kB]
Get: 238 http://deb.debian.org/debian trixie/main armhf gpgconf armhf 2.2.44-1 [104 kB]
Get: 239 http://deb.debian.org/debian trixie/main armhf gpg armhf 2.2.44-1 [461 kB]
Get: 240 http://deb.debian.org/debian trixie/main armhf iso-codes all 4.17.0-1 [3055 kB]
Get: 241 http://deb.debian.org/debian trixie/main armhf libberkeleydb-perl armhf 0.64-2+b3 [109 kB]
Get: 242 http://deb.debian.org/debian trixie/main armhf libclass-xsaccessor-perl armhf 1.19-4+b3 [35.4 kB]
Get: 243 http://deb.debian.org/debian trixie/main armhf libconfig-tiny-perl all 2.30-1 [18.9 kB]
Get: 244 http://deb.debian.org/debian trixie/main armhf libconst-fast-perl all 0.014-2 [8792 B]
Get: 245 http://deb.debian.org/debian trixie/main armhf libcpanel-json-xs-perl armhf 4.38-1 [127 kB]
Get: 246 http://deb.debian.org/debian trixie/main armhf libaliased-perl all 0.34-3 [13.5 kB]
Get: 247 http://deb.debian.org/debian trixie/main armhf libclass-data-inheritable-perl all 0.08-3 [8588 B]
Get: 248 http://deb.debian.org/debian trixie/main armhf libdevel-stacktrace-perl all 2.0500-1 [26.4 kB]
Get: 249 http://deb.debian.org/debian trixie/main armhf libexception-class-perl all 1.45-1 [34.6 kB]
Get: 250 http://deb.debian.org/debian trixie/main armhf libiterator-perl all 0.03+ds1-2 [18.8 kB]
Get: 251 http://deb.debian.org/debian trixie/main armhf libiterator-util-perl all 0.02+ds1-2 [14.0 kB]
Get: 252 http://deb.debian.org/debian trixie/main armhf libdata-dpath-perl all 0.60-1 [41.8 kB]
Get: 253 http://deb.debian.org/debian trixie/main armhf libnet-domain-tld-perl all 1.75-4 [31.5 kB]
Get: 254 http://deb.debian.org/debian trixie/main armhf libdata-validate-domain-perl all 0.15-1 [11.9 kB]
Get: 255 http://deb.debian.org/debian trixie/main armhf libnet-ipv6addr-perl all 1.02-1 [21.7 kB]
Get: 256 http://deb.debian.org/debian trixie/main armhf libnet-netmask-perl all 2.0002-2 [28.6 kB]
Get: 257 http://deb.debian.org/debian trixie/main armhf libnetaddr-ip-perl armhf 4.079+dfsg-2+b3 [96.9 kB]
Get: 258 http://deb.debian.org/debian trixie/main armhf libdata-validate-ip-perl all 0.31-1 [20.6 kB]
Get: 259 http://deb.debian.org/debian trixie/main armhf libdata-validate-uri-perl all 0.07-3 [11.0 kB]
Get: 260 http://deb.debian.org/debian trixie/main armhf libdevel-size-perl armhf 0.84-1 [23.2 kB]
Get: 261 http://deb.debian.org/debian trixie/main armhf libemail-address-xs-perl armhf 1.05-1+b3 [26.5 kB]
Get: 262 http://deb.debian.org/debian trixie/main armhf libipc-system-simple-perl all 1.30-2 [26.8 kB]
Get: 263 http://deb.debian.org/debian trixie/main armhf libfile-basedir-perl all 0.09-2 [15.1 kB]
Get: 264 http://deb.debian.org/debian trixie/main armhf libfile-find-rule-perl all 0.34-3 [26.6 kB]
Get: 265 http://deb.debian.org/debian trixie/main armhf libio-string-perl all 1.08-4 [12.1 kB]
Get: 266 http://deb.debian.org/debian trixie/main armhf libfont-ttf-perl all 1.06-2 [318 kB]
Get: 267 http://deb.debian.org/debian trixie/main armhf libhtml-html5-entities-perl all 0.004-3 [21.0 kB]
Get: 268 http://deb.debian.org/debian trixie/main armhf libhtml-tokeparser-simple-perl all 3.16-4 [39.1 kB]
Get: 269 http://deb.debian.org/debian trixie/main armhf libipc-run3-perl all 0.049-1 [31.5 kB]
Get: 270 http://deb.debian.org/debian trixie/main armhf libjson-maybexs-perl all 1.004008-1 [12.9 kB]
Get: 271 http://deb.debian.org/debian trixie/main armhf liblist-compare-perl all 0.55-2 [65.7 kB]
Get: 272 http://deb.debian.org/debian trixie/main armhf liblist-someutils-perl all 0.59-1 [37.1 kB]
Get: 273 http://deb.debian.org/debian trixie/main armhf liblist-utilsby-perl all 0.12-2 [15.5 kB]
Get: 274 http://deb.debian.org/debian trixie/main armhf libmldbm-perl all 2.05-4 [16.8 kB]
Get: 275 http://deb.debian.org/debian trixie/main armhf libclass-method-modifiers-perl all 2.15-1 [18.0 kB]
Get: 276 http://deb.debian.org/debian trixie/main armhf libimport-into-perl all 1.002005-2 [11.3 kB]
Get: 277 http://deb.debian.org/debian trixie/main armhf librole-tiny-perl all 2.002004-1 [21.4 kB]
Get: 278 http://deb.debian.org/debian trixie/main armhf libsub-quote-perl all 2.006008-1 [21.8 kB]
Get: 279 http://deb.debian.org/debian trixie/main armhf libmoo-perl all 2.005005-1 [58.0 kB]
Get: 280 http://deb.debian.org/debian trixie/main armhf libstrictures-perl all 2.000006-1 [18.6 kB]
Get: 281 http://deb.debian.org/debian trixie/main armhf libmoox-aliases-perl all 0.001006-2 [7156 B]
Get: 282 http://deb.debian.org/debian trixie/main armhf libperlio-gzip-perl armhf 0.20-1+b3 [16.4 kB]
Get: 283 http://deb.debian.org/debian trixie/main armhf libperlio-utf8-strict-perl armhf 0.010-1+b2 [10.9 kB]
Get: 284 http://deb.debian.org/debian trixie/main armhf libproc-processtable-perl armhf 0.636-1+b2 [42.0 kB]
Get: 285 http://deb.debian.org/debian trixie/main armhf libregexp-wildcards-perl all 1.05-3 [14.1 kB]
Get: 286 http://deb.debian.org/debian trixie/main armhf libsereal-decoder-perl armhf 5.004+ds-1+b2 [92.5 kB]
Get: 287 http://deb.debian.org/debian trixie/main armhf libsereal-encoder-perl armhf 5.004+ds-1+b2 [95.0 kB]
Get: 288 http://deb.debian.org/debian trixie/main armhf libterm-readkey-perl armhf 2.38-2+b3 [23.7 kB]
Get: 289 http://deb.debian.org/debian trixie/main armhf libtext-levenshteinxs-perl armhf 0.03-5+b3 [8120 B]
Get: 290 http://deb.debian.org/debian trixie/main armhf libmarkdown2 armhf 2.2.7-2+b1 [28.8 kB]
Get: 291 http://deb.debian.org/debian trixie/main armhf libtext-markdown-discount-perl armhf 0.16-1+b2 [12.7 kB]
Get: 292 http://deb.debian.org/debian trixie/main armhf libdata-messagepack-perl armhf 1.02-1+b3 [30.6 kB]
Get: 293 http://deb.debian.org/debian trixie/main armhf libtext-xslate-perl armhf 3.5.9-2 [169 kB]
Get: 294 http://deb.debian.org/debian trixie/main armhf libtime-duration-perl all 1.21-2 [13.1 kB]
Get: 295 http://deb.debian.org/debian trixie/main armhf libtime-moment-perl armhf 0.44-2+b3 [72.0 kB]
Get: 296 http://deb.debian.org/debian trixie/main armhf libunicode-utf8-perl armhf 0.62-2+b2 [19.2 kB]
Get: 297 http://deb.debian.org/debian trixie/main armhf libcgi-pm-perl all 4.66-1 [217 kB]
Get: 298 http://deb.debian.org/debian trixie/main armhf libhtml-form-perl all 6.11-2 [31.4 kB]
Get: 299 http://deb.debian.org/debian trixie/main armhf libwww-mechanize-perl all 2.19-1 [114 kB]
Get: 300 http://deb.debian.org/debian trixie/main armhf libxml-namespacesupport-perl all 1.12-2 [15.1 kB]
Get: 301 http://deb.debian.org/debian trixie/main armhf libxml-sax-base-perl all 1.09-3 [20.6 kB]
Get: 302 http://deb.debian.org/debian trixie/main armhf libxml-sax-perl all 1.02+dfsg-3 [59.4 kB]
Get: 303 http://deb.debian.org/debian trixie/main armhf libxml-libxml-perl armhf 2.0207+dfsg+really+2.0134-5 [298 kB]
Get: 304 http://deb.debian.org/debian trixie/main armhf liblz1 armhf 1.15~pre1-1 [36.5 kB]
Get: 305 http://deb.debian.org/debian trixie/main armhf plzip armhf 1.11-2 [58.0 kB]
Get: 306 http://deb.debian.org/debian trixie/main armhf liblzo2-2 armhf 2.10-3 [47.6 kB]
Get: 307 http://deb.debian.org/debian trixie/main armhf lzop armhf 1.04-2 [82.2 kB]
Get: 308 http://deb.debian.org/debian trixie/main armhf patchutils armhf 0.4.2-1 [72.5 kB]
Get: 309 http://deb.debian.org/debian trixie/main armhf t1utils armhf 1.41-4 [54.7 kB]
Get: 310 http://deb.debian.org/debian trixie/main armhf unzip armhf 6.0-28 [152 kB]
Get: 311 http://deb.debian.org/debian trixie/main armhf lintian all 2.119.0 [1056 kB]
Get: 312 http://deb.debian.org/debian trixie/main armhf libconfig-model-dpkg-perl all 3.005 [173 kB]
Get: 313 http://deb.debian.org/debian trixie/main armhf libconvert-binhex-perl all 1.125-3 [27.4 kB]
Get: 314 http://deb.debian.org/debian trixie/main armhf libnet-smtp-ssl-perl all 1.04-2 [6548 B]
Get: 315 http://deb.debian.org/debian trixie/main armhf libmailtools-perl all 2.21-3 [88.6 kB]
Get: 316 http://deb.debian.org/debian trixie/main armhf libmime-tools-perl all 5.515-1 [203 kB]
Get: 317 http://deb.debian.org/debian trixie/main armhf libsuitesparseconfig7 armhf 1:7.8.2+dfsg-2 [22.7 kB]
Get: 318 http://deb.debian.org/debian trixie/main armhf libamd3 armhf 1:7.8.2+dfsg-2 [41.5 kB]
Get: 319 http://deb.debian.org/debian trixie/main armhf libblas3 armhf 3.12.0-3 [108 kB]
Get: 320 http://deb.debian.org/debian trixie/main armhf libgfortran5 armhf 14.2.0-6 [263 kB]
Get: 321 http://deb.debian.org/debian trixie/main armhf liblapack3 armhf 3.12.0-3 [1803 kB]
Get: 322 http://deb.debian.org/debian trixie/main armhf libarpack2t64 armhf 3.9.1-1.1+b1 [85.0 kB]
Get: 323 http://deb.debian.org/debian trixie/main armhf libccolamd3 armhf 1:7.8.2+dfsg-2 [40.4 kB]
Get: 324 http://deb.debian.org/debian trixie/main armhf libcamd3 armhf 1:7.8.2+dfsg-2 [38.6 kB]
Get: 325 http://deb.debian.org/debian trixie/main armhf libcolamd3 armhf 1:7.8.2+dfsg-2 [32.9 kB]
Get: 326 http://deb.debian.org/debian trixie/main armhf libcholmod5 armhf 1:7.8.2+dfsg-2 [616 kB]
Get: 327 http://deb.debian.org/debian trixie/main armhf libcxsparse4 armhf 1:7.8.2+dfsg-2 [80.0 kB]
Get: 328 http://deb.debian.org/debian trixie/main armhf libfftw3-double3 armhf 3.3.10-1+b3 [331 kB]
Get: 329 http://deb.debian.org/debian trixie/main armhf libfftw3-single3 armhf 3.3.10-1+b3 [520 kB]
Get: 330 http://deb.debian.org/debian trixie/main armhf libxfixes3 armhf 1:6.0.0-2+b1 [18.6 kB]
Get: 331 http://deb.debian.org/debian trixie/main armhf libxcursor1 armhf 1:1.2.2-1 [34.1 kB]
Get: 332 http://deb.debian.org/debian trixie/main armhf libxft2 armhf 2.3.6-1+b1 [46.4 kB]
Get: 333 http://deb.debian.org/debian trixie/main armhf libxinerama1 armhf 2:1.1.4-3+b1 [15.6 kB]
Get: 334 http://deb.debian.org/debian trixie/main armhf libfltk1.3t64 armhf 1.3.8-6.1 [492 kB]
Get: 335 http://deb.debian.org/debian trixie/main armhf libglvnd0 armhf 1.7.0-1+b1 [52.2 kB]
Get: 336 http://deb.debian.org/debian trixie/main armhf libdrm-common all 2.4.123-1 [8084 B]
Get: 337 http://deb.debian.org/debian trixie/main armhf libdrm2 armhf 2.4.123-1 [34.1 kB]
Get: 338 http://deb.debian.org/debian trixie/main armhf libglapi-mesa armhf 24.2.4-1 [44.7 kB]
Get: 339 http://deb.debian.org/debian trixie/main armhf libx11-xcb1 armhf 2:1.8.7-1+b1 [232 kB]
Get: 340 http://deb.debian.org/debian trixie/main armhf libxcb-dri2-0 armhf 1.17.0-2 [106 kB]
Get: 341 http://deb.debian.org/debian trixie/main armhf libxcb-dri3-0 armhf 1.17.0-2 [106 kB]
Get: 342 http://deb.debian.org/debian trixie/main armhf libxcb-glx0 armhf 1.17.0-2 [120 kB]
Get: 343 http://deb.debian.org/debian trixie/main armhf libxcb-present0 armhf 1.17.0-2 [105 kB]
Get: 344 http://deb.debian.org/debian trixie/main armhf libxcb-randr0 armhf 1.17.0-2 [115 kB]
Get: 345 http://deb.debian.org/debian trixie/main armhf libxcb-sync1 armhf 1.17.0-2 [108 kB]
Get: 346 http://deb.debian.org/debian trixie/main armhf libxcb-xfixes0 armhf 1.17.0-2 [109 kB]
Get: 347 http://deb.debian.org/debian trixie/main armhf libxshmfence1 armhf 1.3-1+b1 [8628 B]
Get: 348 http://deb.debian.org/debian trixie/main armhf libxxf86vm1 armhf 1:1.1.4-1+b3 [18.1 kB]
Get: 349 http://deb.debian.org/debian trixie/main armhf libdrm-amdgpu1 armhf 2.4.123-1 [20.4 kB]
Get: 350 http://deb.debian.org/debian trixie/main armhf libdrm-radeon1 armhf 2.4.123-1 [19.6 kB]
Get: 351 http://deb.debian.org/debian trixie/main armhf libz3-4 armhf 4.8.12-3.1+b2 [6324 kB]
Get: 352 http://deb.debian.org/debian trixie/main armhf libllvm19 armhf 1:19.1.1-1 [23.8 MB]
Get: 353 http://deb.debian.org/debian trixie/main armhf libsensors-config all 1:3.6.0-10 [14.6 kB]
Get: 354 http://deb.debian.org/debian trixie/main armhf libsensors5 armhf 1:3.6.0-10 [32.0 kB]
Get: 355 http://deb.debian.org/debian trixie/main armhf mesa-libgallium armhf 24.2.4-1 [7085 kB]
Get: 356 http://deb.debian.org/debian trixie/main armhf libvulkan1 armhf 1.3.290.0-1 [103 kB]
Get: 357 http://deb.debian.org/debian trixie/main armhf libwayland-server0 armhf 1.23.0-1 [27.6 kB]
Get: 358 http://deb.debian.org/debian trixie/main armhf libgbm1 armhf 24.2.4-1 [38.9 kB]
Get: 359 http://deb.debian.org/debian trixie/main armhf libgl1-mesa-dri armhf 24.2.4-1 [41.0 kB]
Get: 360 http://deb.debian.org/debian trixie/main armhf libglx-mesa0 armhf 24.2.4-1 [132 kB]
Get: 361 http://deb.debian.org/debian trixie/main armhf libglx0 armhf 1.7.0-1+b1 [32.6 kB]
Get: 362 http://deb.debian.org/debian trixie/main armhf libgl1 armhf 1.7.0-1+b1 [91.1 kB]
Get: 363 http://deb.debian.org/debian trixie/main armhf libfltk-gl1.3t64 armhf 1.3.8-6.1 [56.3 kB]
Get: 364 http://deb.debian.org/debian trixie/main armhf libgl2ps1.4 armhf 1.4.2+dfsg1-2 [36.5 kB]
Get: 365 http://deb.debian.org/debian trixie/main armhf libltdl7 armhf 2.4.7-7+b1 [390 kB]
Get: 366 http://deb.debian.org/debian trixie/main armhf libglpk40 armhf 5.0-1+b1 [330 kB]
Get: 367 http://deb.debian.org/debian trixie/main armhf libopengl0 armhf 1.7.0-1+b1 [31.9 kB]
Get: 368 http://deb.debian.org/debian trixie/main armhf libglu1-mesa armhf 9.0.2-1.1+b1 [139 kB]
Get: 369 http://deb.debian.org/debian trixie/main armhf libgif7 armhf 5.2.2-1 [41.2 kB]
Get: 370 http://deb.debian.org/debian trixie/main armhf libhwy1t64 armhf 1.2.0-2 [38.7 kB]
Get: 371 http://deb.debian.org/debian trixie/main armhf liblcms2-2 armhf 2.16-2 [131 kB]
Get: 372 http://deb.debian.org/debian trixie/main armhf libimath-3-1-29t64 armhf 3.1.11-2+b1 [38.2 kB]
Get: 373 http://deb.debian.org/debian trixie/main armhf libopenexr-3-1-30 armhf 3.1.5-5.1+b2 [843 kB]
Get: 374 http://deb.debian.org/debian trixie/main armhf libjxl0.9 armhf 0.9.2-10 [739 kB]
Get: 375 http://deb.debian.org/debian trixie/main armhf libwmflite-0.2-7 armhf 0.2.13-1.1+b2 [69.3 kB]
Get: 376 http://deb.debian.org/debian trixie/main armhf libgraphicsmagick-q16-3t64 armhf 1.4+really1.3.45-1 [1114 kB]
Get: 377 http://deb.debian.org/debian trixie/main armhf libgraphicsmagick++-q16-12t64 armhf 1.4+really1.3.45-1 [109 kB]
Get: 378 http://deb.debian.org/debian trixie/main armhf libcurl4t64 armhf 8.10.1-2 [303 kB]
Get: 379 http://deb.debian.org/debian trixie/main armhf libaec0 armhf 1.1.3-1 [21.5 kB]
Get: 380 http://deb.debian.org/debian trixie/main armhf libsz2 armhf 1.1.3-1 [7724 B]
Get: 381 http://deb.debian.org/debian trixie/main armhf libhdf5-103-1t64 armhf 1.10.10+repack-4 [1196 kB]
Get: 382 http://deb.debian.org/debian trixie/main armhf libasound2-data all 1.2.12-1 [21.0 kB]
Get: 383 http://deb.debian.org/debian trixie/main armhf libasound2t64 armhf 1.2.12-1 [316 kB]
Get: 384 http://deb.debian.org/debian trixie/main armhf libopus0 armhf 1.5.2-2 [2801 kB]
Get: 385 http://deb.debian.org/debian trixie/main armhf libsamplerate0 armhf 0.2.2-4+b1 [947 kB]
Get: 386 http://deb.debian.org/debian trixie/main armhf libjack-jackd2-0 armhf 1.9.22~dfsg-3 [239 kB]
Get: 387 http://deb.debian.org/debian trixie/main armhf libportaudio2 armhf 19.6.0-1.2+b2 [56.7 kB]
Get: 388 http://deb.debian.org/debian trixie/main armhf libqhull-r8.0 armhf 2020.2-6+b1 [219 kB]
Get: 389 http://deb.debian.org/debian trixie/main armhf libqrupdate1 armhf 1.1.5-1 [29.6 kB]
Get: 390 http://deb.debian.org/debian trixie/main armhf libqscintilla2-qt6-l10n all 2.14.1+dfsg-1 [105 kB]
Get: 391 http://deb.debian.org/debian trixie/main armhf libb2-1 armhf 0.98.1-1.1+b1 [21.9 kB]
Get: 392 http://deb.debian.org/debian trixie/main armhf libdouble-conversion3 armhf 3.3.0-1+b1 [38.5 kB]
Get: 393 http://deb.debian.org/debian trixie/main armhf libpcre2-16-0 armhf 10.42-4+b1 [212 kB]
Get: 394 http://deb.debian.org/debian trixie/main armhf libqt6core6t64 armhf 6.6.2+dfsg-12 [1512 kB]
Get: 395 http://deb.debian.org/debian trixie/main armhf libwayland-client0 armhf 1.23.0-1 [20.9 kB]
Get: 396 http://deb.debian.org/debian trixie/main armhf libegl-mesa0 armhf 24.2.4-1 [110 kB]
Get: 397 http://deb.debian.org/debian trixie/main armhf libegl1 armhf 1.7.0-1+b1 [29.1 kB]
Get: 398 http://deb.debian.org/debian trixie/main armhf x11-common all 1:7.7+23.1 [216 kB]
Get: 399 http://deb.debian.org/debian trixie/main armhf libice6 armhf 2:1.0.10-1+b1 [50.1 kB]
Get: 400 http://deb.debian.org/debian trixie/main armhf libevdev2 armhf 1.13.3+dfsg-1 [26.2 kB]
Get: 401 http://deb.debian.org/debian trixie/main armhf libmtdev1t64 armhf 1.1.6-1.2 [21.0 kB]
Get: 402 http://deb.debian.org/debian trixie/main armhf libgudev-1.0-0 armhf 238-5 [12.6 kB]
Get: 403 http://deb.debian.org/debian trixie/main armhf libwacom-common all 2.13.0-1 [98.0 kB]
Get: 404 http://deb.debian.org/debian trixie/main armhf libwacom9 armhf 2.13.0-1 [20.8 kB]
Get: 405 http://deb.debian.org/debian trixie/main armhf libinput-bin armhf 1.26.2-1 [23.6 kB]
Get: 406 http://deb.debian.org/debian trixie/main armhf libinput10 armhf 1.26.2-1 [112 kB]
Get: 407 http://deb.debian.org/debian trixie/main armhf libmd4c0 armhf 0.5.2-2+b1 [43.8 kB]
Get: 408 http://deb.debian.org/debian trixie/main armhf libdbus-1-3 armhf 1.14.10-4+b1 [181 kB]
Get: 409 http://deb.debian.org/debian trixie/main armhf libqt6dbus6 armhf 6.6.2+dfsg-12 [226 kB]
Get: 410 http://deb.debian.org/debian trixie/main armhf libsm6 armhf 2:1.2.3-1+b1 [31.7 kB]
Get: 411 http://deb.debian.org/debian trixie/main armhf libts0t64 armhf 1.22-1.1 [57.6 kB]
Get: 412 http://deb.debian.org/debian trixie/main armhf libxcb-util1 armhf 0.4.0-1+b2 [21.4 kB]
Get: 413 http://deb.debian.org/debian trixie/main armhf libxcb-image0 armhf 0.4.0-2+b1 [21.1 kB]
Get: 414 http://deb.debian.org/debian trixie/main armhf libxcb-render-util0 armhf 0.3.9-1+b2 [16.6 kB]
Get: 415 http://deb.debian.org/debian trixie/main armhf libxcb-cursor0 armhf 0.1.4-1+b1 [16.2 kB]
Get: 416 http://deb.debian.org/debian trixie/main armhf libxcb-icccm4 armhf 0.4.1-1.2 [25.3 kB]
Get: 417 http://deb.debian.org/debian trixie/main armhf libxcb-keysyms1 armhf 0.4.0-1+b3 [15.1 kB]
Get: 418 http://deb.debian.org/debian trixie/main armhf libxcb-shape0 armhf 1.17.0-2 [105 kB]
Get: 419 http://deb.debian.org/debian trixie/main armhf libxcb-xinput0 armhf 1.17.0-2 [127 kB]
Get: 420 http://deb.debian.org/debian trixie/main armhf libxcb-xkb1 armhf 1.17.0-2 [126 kB]
Get: 421 http://deb.debian.org/debian trixie/main armhf xkb-data all 2.42-1 [790 kB]
Get: 422 http://deb.debian.org/debian trixie/main armhf libxkbcommon0 armhf 1.6.0-1+b1 [96.9 kB]
Get: 423 http://deb.debian.org/debian trixie/main armhf libxkbcommon-x11-0 armhf 1.6.0-1+b1 [14.5 kB]
Get: 424 http://deb.debian.org/debian trixie/main armhf libqt6gui6 armhf 6.6.2+dfsg-12 [2581 kB]
Get: 425 http://deb.debian.org/debian trixie/main armhf libavahi-common-data armhf 0.8-13+b2 [112 kB]
Get: 426 http://deb.debian.org/debian trixie/main armhf libavahi-common3 armhf 0.8-13+b2 [40.2 kB]
Get: 427 http://deb.debian.org/debian trixie/main armhf libavahi-client3 armhf 0.8-13+b2 [43.4 kB]
Get: 428 http://deb.debian.org/debian trixie/main armhf libcups2t64 armhf 2.4.10-2 [216 kB]
Get: 429 http://deb.debian.org/debian trixie/main armhf libqt6widgets6 armhf 6.6.2+dfsg-12 [2242 kB]
Get: 430 http://deb.debian.org/debian trixie/main armhf libqt6printsupport6 armhf 6.6.2+dfsg-12 [198 kB]
Get: 431 http://deb.debian.org/debian trixie/main armhf libqscintilla2-qt6-15 armhf 2.14.1+dfsg-1+b3 [965 kB]
Get: 432 http://deb.debian.org/debian trixie/main armhf libqt6core5compat6 armhf 6.6.2-2 [116 kB]
Get: 433 http://deb.debian.org/debian trixie/main armhf libqt6sql6 armhf 6.6.2+dfsg-12 [123 kB]
Get: 434 http://deb.debian.org/debian trixie/main armhf libqt6help6 armhf 6.6.2-4 [158 kB]
Get: 435 http://deb.debian.org/debian trixie/main armhf libduktape207 armhf 2.7.0-2+b1 [114 kB]
Get: 436 http://deb.debian.org/debian trixie/main armhf libproxy1v5 armhf 0.5.8-1 [23.5 kB]
Get: 437 http://deb.debian.org/debian trixie/main armhf libqt6network6 armhf 6.6.2+dfsg-12 [619 kB]
Get: 438 http://deb.debian.org/debian trixie/main armhf libqt6opengl6 armhf 6.6.2+dfsg-12 [356 kB]
Get: 439 http://deb.debian.org/debian trixie/main armhf libqt6openglwidgets6 armhf 6.6.2+dfsg-12 [46.7 kB]
Get: 440 http://deb.debian.org/debian trixie/main armhf libqt6xml6 armhf 6.6.2+dfsg-12 [75.3 kB]
Get: 441 http://deb.debian.org/debian trixie/main armhf libogg0 armhf 1.3.5-3+b1 [21.9 kB]
Get: 442 http://deb.debian.org/debian trixie/main armhf libflac12t64 armhf 1.4.3+ds-2.1 [154 kB]
Get: 443 http://deb.debian.org/debian trixie/main armhf libmp3lame0 armhf 3.100-6+b2 [344 kB]
Get: 444 http://deb.debian.org/debian trixie/main armhf libmpg123-0t64 armhf 1.32.7-1 [134 kB]
Get: 445 http://deb.debian.org/debian trixie/main armhf libvorbis0a armhf 1.3.7-2 [79.0 kB]
Get: 446 http://deb.debian.org/debian trixie/main armhf libvorbisenc2 armhf 1.3.7-2 [69.3 kB]
Get: 447 http://deb.debian.org/debian trixie/main armhf libsndfile1 armhf 1.2.2-1+b2 [181 kB]
Get: 448 http://deb.debian.org/debian trixie/main armhf libspqr4 armhf 1:7.8.2+dfsg-2 [132 kB]
Get: 449 http://deb.debian.org/debian trixie/main armhf libsundials-ida6 armhf 6.4.1+dfsg1-3+b7 [129 kB]
Get: 450 http://deb.debian.org/debian trixie/main armhf libbtf2 armhf 1:7.8.2+dfsg-2 [25.0 kB]
Get: 451 http://deb.debian.org/debian trixie/main armhf libklu2 armhf 1:7.8.2+dfsg-2 [83.8 kB]
Get: 452 http://deb.debian.org/debian trixie/main armhf libfftw3-mpi3 armhf 3.3.10-1+b3 [45.0 kB]
Get: 453 http://deb.debian.org/debian trixie/main armhf libhwloc15 armhf 2.11.2-1 [134 kB]
Get: 454 http://deb.debian.org/debian trixie/main armhf libmpich12 armhf 4.2.0-14 [1498 kB]
Get: 455 http://deb.debian.org/debian trixie/main armhf libhdf5-mpich-103-1t64 armhf 1.10.10+repack-4 [1251 kB]
Get: 456 http://deb.debian.org/debian trixie/main armhf libcombblas2.0.0t64 armhf 2.0.0-3.1+b2 [256 kB]
Get: 457 http://deb.debian.org/debian trixie/main armhf libmetis5 armhf 5.1.0.dfsg-7+b1 [139 kB]
Get: 458 http://deb.debian.org/debian trixie/main armhf libptscotch-7.0 armhf 7.0.5-1 [647 kB]
Get: 459 http://deb.debian.org/debian trixie/main armhf libsuperlu-dist8 armhf 8.2.1+dfsg1-2 [473 kB]
Get: 460 http://deb.debian.org/debian trixie/main armhf libhypre-2.29.0 armhf 2.29.0-2+b1 [1218 kB]
Get: 461 http://deb.debian.org/debian trixie/main armhf hwloc-nox armhf 2.11.2-1 [201 kB]
Get: 462 http://deb.debian.org/debian trixie/main armhf libslurm41t64 armhf 24.05.2-1 [682 kB]
Get: 463 http://deb.debian.org/debian trixie/main armhf mpich armhf 4.2.0-14 [223 kB]
Get: 464 http://deb.debian.org/debian trixie/main armhf mpi-default-bin armhf 1.17 [2368 B]
Get: 465 http://deb.debian.org/debian trixie/main armhf libscalapack-mpich2.2 armhf 2.2.1-5 [1258 kB]
Get: 466 http://deb.debian.org/debian trixie/main armhf libmumps-5.6t64 armhf 5.6.2-2.1+b2 [1685 kB]
Get: 467 http://deb.debian.org/debian trixie/main armhf libsuperlu6 armhf 6.0.1+dfsg1-1+b1 [139 kB]
Get: 468 http://deb.debian.org/debian trixie/main armhf libumfpack6 armhf 1:7.8.2+dfsg-2 [274 kB]
Get: 469 http://deb.debian.org/debian trixie/main armhf ocl-icd-libopencl1 armhf 2.3.2-1+b1 [37.3 kB]
Get: 470 http://deb.debian.org/debian trixie/main armhf libpetsc-real3.20 armhf 3.20.6+dfsg1-3 [5700 kB]
Get: 471 http://deb.debian.org/debian trixie/main armhf libsundials-nvecparallel-petsc6 armhf 6.4.1+dfsg1-3+b7 [55.8 kB]
Get: 472 http://deb.debian.org/debian trixie/main armhf libsundials-sunmatrix4 armhf 6.4.1+dfsg1-3+b7 [90.7 kB]
Get: 473 http://deb.debian.org/debian trixie/main armhf libsundials-sunlinsol3 armhf 6.4.1+dfsg1-3+b7 [162 kB]
Get: 474 http://deb.debian.org/debian trixie/main armhf libtext-unidecode-perl all 1.30-3 [101 kB]
Get: 475 http://deb.debian.org/debian trixie/main armhf texinfo-lib armhf 7.1.1-1 [209 kB]
Get: 476 http://deb.debian.org/debian trixie/main armhf tex-common all 6.18 [32.5 kB]
Get: 477 http://deb.debian.org/debian trixie/main armhf texinfo all 7.1.1-1 [1753 kB]
Get: 478 http://deb.debian.org/debian trixie/main armhf octave-common all 9.2.0-3 [6591 kB]
Get: 479 http://deb.debian.org/debian trixie/main armhf octave armhf 9.2.0-3 [8476 kB]
Get: 480 http://deb.debian.org/debian trixie/main armhf libncurses6 armhf 6.5-2 [81.8 kB]
Get: 481 http://deb.debian.org/debian trixie/main armhf libncurses-dev armhf 6.5-2 [309 kB]
Get: 482 http://deb.debian.org/debian trixie/main armhf libreadline-dev armhf 8.2-5 [138 kB]
Get: 483 http://deb.debian.org/debian trixie/main armhf libhdf5-fortran-102t64 armhf 1.10.10+repack-4 [78.5 kB]
Get: 484 http://deb.debian.org/debian trixie/main armhf libhdf5-hl-100t64 armhf 1.10.10+repack-4 [62.3 kB]
Get: 485 http://deb.debian.org/debian trixie/main armhf libhdf5-hl-fortran-100t64 armhf 1.10.10+repack-4 [35.1 kB]
Get: 486 http://deb.debian.org/debian trixie/main armhf libhdf5-cpp-103-1t64 armhf 1.10.10+repack-4 [121 kB]
Get: 487 http://deb.debian.org/debian trixie/main armhf libhdf5-hl-cpp-100t64 armhf 1.10.10+repack-4 [21.0 kB]
Get: 488 http://deb.debian.org/debian trixie/main armhf zlib1g-dev armhf 1:1.3.dfsg+really1.3.1-1 [904 kB]
Get: 489 http://deb.debian.org/debian trixie/main armhf libjpeg62-turbo-dev armhf 1:2.1.5-3 [260 kB]
Get: 490 http://deb.debian.org/debian trixie/main armhf libjpeg-dev armhf 1:2.1.5-3 [71.9 kB]
Get: 491 http://deb.debian.org/debian trixie/main armhf libaec-dev armhf 1.1.3-1 [18.8 kB]
Get: 492 http://deb.debian.org/debian trixie/main armhf libbrotli-dev armhf 1.1.0-2+b4 [292 kB]
Get: 493 http://deb.debian.org/debian trixie/main armhf libidn2-dev armhf 2.3.7-2 [119 kB]
Get: 494 http://deb.debian.org/debian trixie/main armhf libpkgconf3 armhf 1.8.1-3 [31.7 kB]
Get: 495 http://deb.debian.org/debian trixie/main armhf pkgconf-bin armhf 1.8.1-3 [29.1 kB]
Get: 496 http://deb.debian.org/debian trixie/main armhf pkgconf armhf 1.8.1-3 [26.1 kB]
Get: 497 http://deb.debian.org/debian trixie/main armhf libnghttp2-dev armhf 1.63.0-1 [103 kB]
Get: 498 http://deb.debian.org/debian trixie/main armhf libpsl-dev armhf 0.21.2-1.1 [76.6 kB]
Get: 499 http://deb.debian.org/debian trixie/main armhf libgmpxx4ldbl armhf 2:6.3.0+dfsg-2+b1 [328 kB]
Get: 500 http://deb.debian.org/debian trixie/main armhf libgmp-dev armhf 2:6.3.0+dfsg-2+b1 [591 kB]
Get: 501 http://deb.debian.org/debian trixie/main armhf libevent-2.1-7t64 armhf 2.1.12-stable-10 [164 kB]
Get: 502 http://deb.debian.org/debian trixie/main armhf libunbound8 armhf 1.21.1-1 [534 kB]
Get: 503 http://deb.debian.org/debian trixie/main armhf libgnutls-dane0t64 armhf 3.8.6-2 [432 kB]
Get: 504 http://deb.debian.org/debian trixie/main armhf libgnutls-openssl27t64 armhf 3.8.6-2 [432 kB]
Get: 505 http://deb.debian.org/debian trixie/main armhf libp11-kit-dev armhf 0.25.5-2 [208 kB]
Get: 506 http://deb.debian.org/debian trixie/main armhf libtasn1-6-dev armhf 4.19.0-3+b2 [93.0 kB]
Get: 507 http://deb.debian.org/debian trixie/main armhf nettle-dev armhf 3.10-1 [1313 kB]
Get: 508 http://deb.debian.org/debian trixie/main armhf libgnutls28-dev armhf 3.8.6-2 [1319 kB]
Get: 509 http://deb.debian.org/debian trixie/main armhf librtmp-dev armhf 2.4+20151223.gitfa8646d.1-2+b4 [63.5 kB]
Get: 510 http://deb.debian.org/debian trixie/main armhf libssl-dev armhf 3.3.2-1 [2384 kB]
Get: 511 http://deb.debian.org/debian trixie/main armhf libssh2-1-dev armhf 1.11.0-7 [342 kB]
Get: 512 http://deb.debian.org/debian trixie/main armhf libzstd-dev armhf 1.5.6+dfsg-1 [326 kB]
Get: 513 http://deb.debian.org/debian trixie/main armhf libcurl4-openssl-dev armhf 8.10.1-2 [423 kB]
Get: 514 http://deb.debian.org/debian trixie/main armhf hdf5-helpers armhf 1.10.10+repack-4 [23.5 kB]
Get: 515 http://deb.debian.org/debian trixie/main armhf libhdf5-dev armhf 1.10.10+repack-4 [2616 kB]
Get: 516 http://deb.debian.org/debian trixie/main armhf xorg-sgml-doctools all 1:1.11-1.1 [22.1 kB]
Get: 517 http://deb.debian.org/debian trixie/main armhf x11proto-dev all 2024.1-1 [603 kB]
Get: 518 http://deb.debian.org/debian trixie/main armhf libxau-dev armhf 1:1.0.9-1+b1 [20.8 kB]
Get: 519 http://deb.debian.org/debian trixie/main armhf libxdmcp-dev armhf 1:1.1.2-3+b1 [39.3 kB]
Get: 520 http://deb.debian.org/debian trixie/main armhf xtrans-dev all 1.4.0-1 [98.7 kB]
Get: 521 http://deb.debian.org/debian trixie/main armhf libxcb1-dev armhf 1.17.0-2 [180 kB]
Get: 522 http://deb.debian.org/debian trixie/main armhf libx11-dev armhf 2:1.8.7-1+b1 [818 kB]
Get: 523 http://deb.debian.org/debian trixie/main armhf libglx-dev armhf 1.7.0-1+b1 [15.8 kB]
Get: 524 http://deb.debian.org/debian trixie/main armhf libgl-dev armhf 1.7.0-1+b1 [101 kB]
Get: 525 http://deb.debian.org/debian trixie/main armhf libblas-dev armhf 3.12.0-3 [119 kB]
Get: 526 http://deb.debian.org/debian trixie/main armhf liblapack-dev armhf 3.12.0-3 [1903 kB]
Get: 527 http://deb.debian.org/debian trixie/main armhf libfftw3-bin armhf 3.3.10-1+b3 [42.0 kB]
Get: 528 http://deb.debian.org/debian trixie/main armhf libfftw3-dev armhf 3.3.10-1+b3 [834 kB]
Get: 529 http://deb.debian.org/debian trixie/main armhf libgfortran-14-dev armhf 14.2.0-6 [315 kB]
Get: 530 http://deb.debian.org/debian trixie/main armhf gfortran-14-arm-linux-gnueabihf armhf 14.2.0-6 [8718 kB]
Get: 531 http://deb.debian.org/debian trixie/main armhf gfortran-14 armhf 14.2.0-6 [11.9 kB]
Get: 532 http://deb.debian.org/debian trixie/main armhf gfortran-arm-linux-gnueabihf armhf 4:14.2.0-1 [1288 B]
Get: 533 http://deb.debian.org/debian trixie/main armhf gfortran armhf 4:14.2.0-1 [1428 B]
Get: 534 http://deb.debian.org/debian trixie/main armhf octave-dev armhf 9.2.0-3 [995 kB]
Get: 535 http://deb.debian.org/debian trixie/main armhf dh-octave all 1.8.0 [22.7 kB]
Get: 536 http://deb.debian.org/debian trixie/main armhf octave-io armhf 2.6.4-3+b2 [198 kB]
Fetched 189 MB in 4s (42.5 MB/s)
debconf: delaying package configuration, since apt-utils is not installed
Selecting previously unselected package netbase.
(Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 19520 files and directories currently installed.)
Preparing to unpack .../000-netbase_6.4_all.deb ...
Unpacking netbase (6.4) ...
Selecting previously unselected package readline-common.
Preparing to unpack .../001-readline-common_8.2-5_all.deb ...
Unpacking readline-common (8.2-5) ...
Selecting previously unselected package sensible-utils.
Preparing to unpack .../002-sensible-utils_0.0.24_all.deb ...
Unpacking sensible-utils (0.0.24) ...
Selecting previously unselected package openssl.
Preparing to unpack .../003-openssl_3.3.2-1_armhf.deb ...
Unpacking openssl (3.3.2-1) ...
Selecting previously unselected package ca-certificates.
Preparing to unpack .../004-ca-certificates_20240203_all.deb ...
Unpacking ca-certificates (20240203) ...
Selecting previously unselected package libmagic-mgc.
Preparing to unpack .../005-libmagic-mgc_1%3a5.45-3_armhf.deb ...
Unpacking libmagic-mgc (1:5.45-3) ...
Selecting previously unselected package libmagic1t64:armhf.
Preparing to unpack .../006-libmagic1t64_1%3a5.45-3_armhf.deb ...
Unpacking libmagic1t64:armhf (1:5.45-3) ...
Selecting previously unselected package file.
Preparing to unpack .../007-file_1%3a5.45-3_armhf.deb ...
Unpacking file (1:5.45-3) ...
Selecting previously unselected package gettext-base.
Preparing to unpack .../008-gettext-base_0.22.5-2_armhf.deb ...
Unpacking gettext-base (0.22.5-2) ...
Selecting previously unselected package libuchardet0:armhf.
Preparing to unpack .../009-libuchardet0_0.0.8-1+b1_armhf.deb ...
Unpacking libuchardet0:armhf (0.0.8-1+b1) ...
Selecting previously unselected package groff-base.
Preparing to unpack .../010-groff-base_1.23.0-5_armhf.deb ...
Unpacking groff-base (1.23.0-5) ...
Selecting previously unselected package bsdextrautils.
Preparing to unpack .../011-bsdextrautils_2.40.2-9_armhf.deb ...
Unpacking bsdextrautils (2.40.2-9) ...
Selecting previously unselected package libpipeline1:armhf.
Preparing to unpack .../012-libpipeline1_1.5.8-1_armhf.deb ...
Unpacking libpipeline1:armhf (1.5.8-1) ...
Selecting previously unselected package man-db.
Preparing to unpack .../013-man-db_2.13.0-1_armhf.deb ...
Unpacking man-db (2.13.0-1) ...
Selecting previously unselected package ucf.
Preparing to unpack .../014-ucf_3.0043+nmu1_all.deb ...
Moving old data out of the way
Unpacking ucf (3.0043+nmu1) ...
Selecting previously unselected package aglfn.
Preparing to unpack .../015-aglfn_1.7+git20191031.4036a9c-2_all.deb ...
Unpacking aglfn (1.7+git20191031.4036a9c-2) ...
Selecting previously unselected package libglib2.0-0t64:armhf.
Preparing to unpack .../016-libglib2.0-0t64_2.82.1-1_armhf.deb ...
Unpacking libglib2.0-0t64:armhf (2.82.1-1) ...
Selecting previously unselected package libicu72:armhf.
Preparing to unpack .../017-libicu72_72.1-5_armhf.deb ...
Unpacking libicu72:armhf (72.1-5) ...
Selecting previously unselected package libxml2:armhf.
Preparing to unpack .../018-libxml2_2.12.7+dfsg+really2.9.14-0.1_armhf.deb ...
Unpacking libxml2:armhf (2.12.7+dfsg+really2.9.14-0.1) ...
Selecting previously unselected package shared-mime-info.
Preparing to unpack .../019-shared-mime-info_2.4-5+b1_armhf.deb ...
Unpacking shared-mime-info (2.4-5+b1) ...
Selecting previously unselected package libbrotli1:armhf.
Preparing to unpack .../020-libbrotli1_1.1.0-2+b4_armhf.deb ...
Unpacking libbrotli1:armhf (1.1.0-2+b4) ...
Selecting previously unselected package libkrb5support0:armhf.
Preparing to unpack .../021-libkrb5support0_1.21.3-3_armhf.deb ...
Unpacking libkrb5support0:armhf (1.21.3-3) ...
Selecting previously unselected package libcom-err2:armhf.
Preparing to unpack .../022-libcom-err2_1.47.1-1_armhf.deb ...
Unpacking libcom-err2:armhf (1.47.1-1) ...
Selecting previously unselected package libk5crypto3:armhf.
Preparing to unpack .../023-libk5crypto3_1.21.3-3_armhf.deb ...
Unpacking libk5crypto3:armhf (1.21.3-3) ...
Selecting previously unselected package libkeyutils1:armhf.
Preparing to unpack .../024-libkeyutils1_1.6.3-3_armhf.deb ...
Unpacking libkeyutils1:armhf (1.6.3-3) ...
Selecting previously unselected package libkrb5-3:armhf.
Preparing to unpack .../025-libkrb5-3_1.21.3-3_armhf.deb ...
Unpacking libkrb5-3:armhf (1.21.3-3) ...
Selecting previously unselected package libgssapi-krb5-2:armhf.
Preparing to unpack .../026-libgssapi-krb5-2_1.21.3-3_armhf.deb ...
Unpacking libgssapi-krb5-2:armhf (1.21.3-3) ...
Selecting previously unselected package libsasl2-modules-db:armhf.
Preparing to unpack .../027-libsasl2-modules-db_2.1.28+dfsg1-8_armhf.deb ...
Unpacking libsasl2-modules-db:armhf (2.1.28+dfsg1-8) ...
Selecting previously unselected package libsasl2-2:armhf.
Preparing to unpack .../028-libsasl2-2_2.1.28+dfsg1-8_armhf.deb ...
Unpacking libsasl2-2:armhf (2.1.28+dfsg1-8) ...
Selecting previously unselected package libldap-2.5-0:armhf.
Preparing to unpack .../029-libldap-2.5-0_2.5.18+dfsg-3_armhf.deb ...
Unpacking libldap-2.5-0:armhf (2.5.18+dfsg-3) ...
Selecting previously unselected package libnghttp2-14:armhf.
Preparing to unpack .../030-libnghttp2-14_1.63.0-1_armhf.deb ...
Unpacking libnghttp2-14:armhf (1.63.0-1) ...
Selecting previously unselected package libnghttp3-9:armhf.
Preparing to unpack .../031-libnghttp3-9_1.4.0-1_armhf.deb ...
Unpacking libnghttp3-9:armhf (1.4.0-1) ...
Selecting previously unselected package libngtcp2-16:armhf.
Preparing to unpack .../032-libngtcp2-16_1.6.0-1_armhf.deb ...
Unpacking libngtcp2-16:armhf (1.6.0-1) ...
Selecting previously unselected package libngtcp2-crypto-gnutls8:armhf.
Preparing to unpack .../033-libngtcp2-crypto-gnutls8_1.6.0-1_armhf.deb ...
Unpacking libngtcp2-crypto-gnutls8:armhf (1.6.0-1) ...
Selecting previously unselected package libpsl5t64:armhf.
Preparing to unpack .../034-libpsl5t64_0.21.2-1.1_armhf.deb ...
Unpacking libpsl5t64:armhf (0.21.2-1.1) ...
Selecting previously unselected package librtmp1:armhf.
Preparing to unpack .../035-librtmp1_2.4+20151223.gitfa8646d.1-2+b4_armhf.deb ...
Unpacking librtmp1:armhf (2.4+20151223.gitfa8646d.1-2+b4) ...
Selecting previously unselected package libssh2-1t64:armhf.
Preparing to unpack .../036-libssh2-1t64_1.11.0-7_armhf.deb ...
Unpacking libssh2-1t64:armhf (1.11.0-7) ...
Selecting previously unselected package libcurl3t64-gnutls:armhf.
Preparing to unpack .../037-libcurl3t64-gnutls_8.10.1-2_armhf.deb ...
Unpacking libcurl3t64-gnutls:armhf (8.10.1-2) ...
Selecting previously unselected package libstemmer0d:armhf.
Preparing to unpack .../038-libstemmer0d_2.2.0-4+b1_armhf.deb ...
Unpacking libstemmer0d:armhf (2.2.0-4+b1) ...
Selecting previously unselected package libxmlb2:armhf.
Preparing to unpack .../039-libxmlb2_0.3.20-1_armhf.deb ...
Unpacking libxmlb2:armhf (0.3.20-1) ...
Selecting previously unselected package libyaml-0-2:armhf.
Preparing to unpack .../040-libyaml-0-2_0.2.5-1+b1_armhf.deb ...
Unpacking libyaml-0-2:armhf (0.2.5-1+b1) ...
Selecting previously unselected package libappstream5:armhf.
Preparing to unpack .../041-libappstream5_1.0.3-1_armhf.deb ...
Unpacking libappstream5:armhf (1.0.3-1) ...
Selecting previously unselected package appstream.
Preparing to unpack .../042-appstream_1.0.3-1_armhf.deb ...
Unpacking appstream (1.0.3-1) ...
Selecting previously unselected package m4.
Preparing to unpack .../043-m4_1.4.19-4_armhf.deb ...
Unpacking m4 (1.4.19-4) ...
Selecting previously unselected package autoconf.
Preparing to unpack .../044-autoconf_2.72-3_all.deb ...
Unpacking autoconf (2.72-3) ...
Selecting previously unselected package autotools-dev.
Preparing to unpack .../045-autotools-dev_20220109.1_all.deb ...
Unpacking autotools-dev (20220109.1) ...
Selecting previously unselected package automake.
Preparing to unpack .../046-automake_1%3a1.16.5-1.3_all.deb ...
Unpacking automake (1:1.16.5-1.3) ...
Selecting previously unselected package autopoint.
Preparing to unpack .../047-autopoint_0.22.5-2_all.deb ...
Unpacking autopoint (0.22.5-2) ...
Selecting previously unselected package libcapture-tiny-perl.
Preparing to unpack .../048-libcapture-tiny-perl_0.48-2_all.deb ...
Unpacking libcapture-tiny-perl (0.48-2) ...
Selecting previously unselected package libparams-util-perl.
Preparing to unpack .../049-libparams-util-perl_1.102-3_armhf.deb ...
Unpacking libparams-util-perl (1.102-3) ...
Selecting previously unselected package libsub-install-perl.
Preparing to unpack .../050-libsub-install-perl_0.929-1_all.deb ...
Unpacking libsub-install-perl (0.929-1) ...
Selecting previously unselected package libdata-optlist-perl.
Preparing to unpack .../051-libdata-optlist-perl_0.114-1_all.deb ...
Unpacking libdata-optlist-perl (0.114-1) ...
Selecting previously unselected package libb-hooks-op-check-perl:armhf.
Preparing to unpack .../052-libb-hooks-op-check-perl_0.22-3+b1_armhf.deb ...
Unpacking libb-hooks-op-check-perl:armhf (0.22-3+b1) ...
Selecting previously unselected package libdynaloader-functions-perl.
Preparing to unpack .../053-libdynaloader-functions-perl_0.004-1_all.deb ...
Unpacking libdynaloader-functions-perl (0.004-1) ...
Selecting previously unselected package libdevel-callchecker-perl:armhf.
Preparing to unpack .../054-libdevel-callchecker-perl_0.009-1_armhf.deb ...
Unpacking libdevel-callchecker-perl:armhf (0.009-1) ...
Selecting previously unselected package libparams-classify-perl:armhf.
Preparing to unpack .../055-libparams-classify-perl_0.015-2+b3_armhf.deb ...
Unpacking libparams-classify-perl:armhf (0.015-2+b3) ...
Selecting previously unselected package libmodule-runtime-perl.
Preparing to unpack .../056-libmodule-runtime-perl_0.016-2_all.deb ...
Unpacking libmodule-runtime-perl (0.016-2) ...
Selecting previously unselected package libtry-tiny-perl.
Preparing to unpack .../057-libtry-tiny-perl_0.32-1_all.deb ...
Unpacking libtry-tiny-perl (0.32-1) ...
Selecting previously unselected package libmodule-implementation-perl.
Preparing to unpack .../058-libmodule-implementation-perl_0.09-2_all.deb ...
Unpacking libmodule-implementation-perl (0.09-2) ...
Selecting previously unselected package libpackage-stash-perl.
Preparing to unpack .../059-libpackage-stash-perl_0.40-1_all.deb ...
Unpacking libpackage-stash-perl (0.40-1) ...
Selecting previously unselected package libclass-load-perl.
Preparing to unpack .../060-libclass-load-perl_0.25-2_all.deb ...
Unpacking libclass-load-perl (0.25-2) ...
Selecting previously unselected package libio-stringy-perl.
Preparing to unpack .../061-libio-stringy-perl_2.113-2_all.deb ...
Unpacking libio-stringy-perl (2.113-2) ...
Selecting previously unselected package libparams-validate-perl:armhf.
Preparing to unpack .../062-libparams-validate-perl_1.31-2+b2_armhf.deb ...
Unpacking libparams-validate-perl:armhf (1.31-2+b2) ...
Selecting previously unselected package libsub-exporter-perl.
Preparing to unpack .../063-libsub-exporter-perl_0.990-1_all.deb ...
Unpacking libsub-exporter-perl (0.990-1) ...
Selecting previously unselected package libgetopt-long-descriptive-perl.
Preparing to unpack .../064-libgetopt-long-descriptive-perl_0.111-1_all.deb ...
Unpacking libgetopt-long-descriptive-perl (0.111-1) ...
Selecting previously unselected package libio-tiecombine-perl.
Preparing to unpack .../065-libio-tiecombine-perl_1.005-3_all.deb ...
Unpacking libio-tiecombine-perl (1.005-3) ...
Selecting previously unselected package libmodule-pluggable-perl.
Preparing to unpack .../066-libmodule-pluggable-perl_5.2-5_all.deb ...
Unpacking libmodule-pluggable-perl (5.2-5) ...
Selecting previously unselected package libstring-rewriteprefix-perl.
Preparing to unpack .../067-libstring-rewriteprefix-perl_0.009-1_all.deb ...
Unpacking libstring-rewriteprefix-perl (0.009-1) ...
Selecting previously unselected package libapp-cmd-perl.
Preparing to unpack .../068-libapp-cmd-perl_0.336-1_all.deb ...
Unpacking libapp-cmd-perl (0.336-1) ...
Selecting previously unselected package libboolean-perl.
Preparing to unpack .../069-libboolean-perl_0.46-3_all.deb ...
Unpacking libboolean-perl (0.46-3) ...
Selecting previously unselected package libsub-uplevel-perl.
Preparing to unpack .../070-libsub-uplevel-perl_0.2800-3_all.deb ...
Unpacking libsub-uplevel-perl (0.2800-3) ...
Selecting previously unselected package libtest-exception-perl.
Preparing to unpack .../071-libtest-exception-perl_0.43-3_all.deb ...
Unpacking libtest-exception-perl (0.43-3) ...
Selecting previously unselected package libcarp-assert-more-perl.
Preparing to unpack .../072-libcarp-assert-more-perl_2.4.0-1_all.deb ...
Unpacking libcarp-assert-more-perl (2.4.0-1) ...
Selecting previously unselected package libfile-which-perl.
Preparing to unpack .../073-libfile-which-perl_1.27-2_all.deb ...
Unpacking libfile-which-perl (1.27-2) ...
Selecting previously unselected package libfile-homedir-perl.
Preparing to unpack .../074-libfile-homedir-perl_1.006-2_all.deb ...
Unpacking libfile-homedir-perl (1.006-2) ...
Selecting previously unselected package libclone-choose-perl.
Preparing to unpack .../075-libclone-choose-perl_0.010-2_all.deb ...
Unpacking libclone-choose-perl (0.010-2) ...
Selecting previously unselected package libhash-merge-perl.
Preparing to unpack .../076-libhash-merge-perl_0.302-1_all.deb ...
Unpacking libhash-merge-perl (0.302-1) ...
Selecting previously unselected package libjson-perl.
Preparing to unpack .../077-libjson-perl_4.10000-1_all.deb ...
Unpacking libjson-perl (4.10000-1) ...
Selecting previously unselected package libexporter-tiny-perl.
Preparing to unpack .../078-libexporter-tiny-perl_1.006002-1_all.deb ...
Unpacking libexporter-tiny-perl (1.006002-1) ...
Selecting previously unselected package liblist-moreutils-xs-perl.
Preparing to unpack .../079-liblist-moreutils-xs-perl_0.430-4_armhf.deb ...
Unpacking liblist-moreutils-xs-perl (0.430-4) ...
Selecting previously unselected package liblist-moreutils-perl.
Preparing to unpack .../080-liblist-moreutils-perl_0.430-2_all.deb ...
Unpacking liblist-moreutils-perl (0.430-2) ...
Selecting previously unselected package liblog-log4perl-perl.
Preparing to unpack .../081-liblog-log4perl-perl_1.57-1_all.deb ...
Unpacking liblog-log4perl-perl (1.57-1) ...
Selecting previously unselected package libmouse-perl:armhf.
Preparing to unpack .../082-libmouse-perl_2.5.11-1_armhf.deb ...
Unpacking libmouse-perl:armhf (2.5.11-1) ...
Selecting previously unselected package libmousex-nativetraits-perl.
Preparing to unpack .../083-libmousex-nativetraits-perl_1.09-3_all.deb ...
Unpacking libmousex-nativetraits-perl (1.09-3) ...
Selecting previously unselected package libmousex-strictconstructor-perl.
Preparing to unpack .../084-libmousex-strictconstructor-perl_0.02-3_all.deb ...
Unpacking libmousex-strictconstructor-perl (0.02-3) ...
Selecting previously unselected package libparse-recdescent-perl.
Preparing to unpack .../085-libparse-recdescent-perl_1.967015+dfsg-4_all.deb ...
Unpacking libparse-recdescent-perl (1.967015+dfsg-4) ...
Selecting previously unselected package libpath-tiny-perl.
Preparing to unpack .../086-libpath-tiny-perl_0.146-1_all.deb ...
Unpacking libpath-tiny-perl (0.146-1) ...
Selecting previously unselected package libpod-pom-perl.
Preparing to unpack .../087-libpod-pom-perl_2.01-4_all.deb ...
Unpacking libpod-pom-perl (2.01-4) ...
Selecting previously unselected package libregexp-common-perl.
Preparing to unpack .../088-libregexp-common-perl_2024080801-1_all.deb ...
Unpacking libregexp-common-perl (2024080801-1) ...
Selecting previously unselected package libyaml-tiny-perl.
Preparing to unpack .../089-libyaml-tiny-perl_1.74-1_all.deb ...
Unpacking libyaml-tiny-perl (1.74-1) ...
Selecting previously unselected package libconfig-model-perl.
Preparing to unpack .../090-libconfig-model-perl_2.154-1_all.deb ...
Unpacking libconfig-model-perl (2.154-1) ...
Selecting previously unselected package libyaml-pp-perl.
Preparing to unpack .../091-libyaml-pp-perl_0.38.0-1_all.deb ...
Unpacking libyaml-pp-perl (0.38.0-1) ...
Selecting previously unselected package cme.
Preparing to unpack .../092-cme_1.040-1_all.deb ...
Unpacking cme (1.040-1) ...
Selecting previously unselected package libdebhelper-perl.
Preparing to unpack .../093-libdebhelper-perl_13.20_all.deb ...
Unpacking libdebhelper-perl (13.20) ...
Selecting previously unselected package libtool.
Preparing to unpack .../094-libtool_2.4.7-7_all.deb ...
Unpacking libtool (2.4.7-7) ...
Selecting previously unselected package dh-autoreconf.
Preparing to unpack .../095-dh-autoreconf_20_all.deb ...
Unpacking dh-autoreconf (20) ...
Selecting previously unselected package libarchive-zip-perl.
Preparing to unpack .../096-libarchive-zip-perl_1.68-1_all.deb ...
Unpacking libarchive-zip-perl (1.68-1) ...
Selecting previously unselected package libfile-stripnondeterminism-perl.
Preparing to unpack .../097-libfile-stripnondeterminism-perl_1.14.0-1_all.deb ...
Unpacking libfile-stripnondeterminism-perl (1.14.0-1) ...
Selecting previously unselected package dh-strip-nondeterminism.
Preparing to unpack .../098-dh-strip-nondeterminism_1.14.0-1_all.deb ...
Unpacking dh-strip-nondeterminism (1.14.0-1) ...
Selecting previously unselected package libelf1t64:armhf.
Preparing to unpack .../099-libelf1t64_0.191-2_armhf.deb ...
Unpacking libelf1t64:armhf (0.191-2) ...
Selecting previously unselected package dwz.
Preparing to unpack .../100-dwz_0.15-1+b2_armhf.deb ...
Unpacking dwz (0.15-1+b2) ...
Selecting previously unselected package gettext.
Preparing to unpack .../101-gettext_0.22.5-2_armhf.deb ...
Unpacking gettext (0.22.5-2) ...
Selecting previously unselected package intltool-debian.
Preparing to unpack .../102-intltool-debian_0.35.0+20060710.6_all.deb ...
Unpacking intltool-debian (0.35.0+20060710.6) ...
Selecting previously unselected package po-debconf.
Preparing to unpack .../103-po-debconf_1.0.21+nmu1_all.deb ...
Unpacking po-debconf (1.0.21+nmu1) ...
Selecting previously unselected package debhelper.
Preparing to unpack .../104-debhelper_13.20_all.deb ...
Unpacking debhelper (13.20) ...
Selecting previously unselected package gnuplot-data.
Preparing to unpack .../105-gnuplot-data_6.0.0+dfsg1-3_all.deb ...
Unpacking gnuplot-data (6.0.0+dfsg1-3) ...
Selecting previously unselected package libexpat1:armhf.
Preparing to unpack .../106-libexpat1_2.6.3-1_armhf.deb ...
Unpacking libexpat1:armhf (2.6.3-1) ...
Selecting previously unselected package libpng16-16t64:armhf.
Preparing to unpack .../107-libpng16-16t64_1.6.44-2_armhf.deb ...
Unpacking libpng16-16t64:armhf (1.6.44-2) ...
Selecting previously unselected package libfreetype6:armhf.
Preparing to unpack .../108-libfreetype6_2.13.3+dfsg-1_armhf.deb ...
Unpacking libfreetype6:armhf (2.13.3+dfsg-1) ...
Selecting previously unselected package fonts-dejavu-mono.
Preparing to unpack .../109-fonts-dejavu-mono_2.37-8_all.deb ...
Unpacking fonts-dejavu-mono (2.37-8) ...
Selecting previously unselected package fonts-dejavu-core.
Preparing to unpack .../110-fonts-dejavu-core_2.37-8_all.deb ...
Unpacking fonts-dejavu-core (2.37-8) ...
Selecting previously unselected package fonts-freefont-otf.
Preparing to unpack .../111-fonts-freefont-otf_20211204+svn4273-2_all.deb ...
Unpacking fonts-freefont-otf (20211204+svn4273-2) ...
Selecting previously unselected package fontconfig-config.
Preparing to unpack .../112-fontconfig-config_2.15.0-1.1_armhf.deb ...
Unpacking fontconfig-config (2.15.0-1.1) ...
Selecting previously unselected package libfontconfig1:armhf.
Preparing to unpack .../113-libfontconfig1_2.15.0-1.1_armhf.deb ...
Unpacking libfontconfig1:armhf (2.15.0-1.1) ...
Selecting previously unselected package libpixman-1-0:armhf.
Preparing to unpack .../114-libpixman-1-0_0.42.2-1+b1_armhf.deb ...
Unpacking libpixman-1-0:armhf (0.42.2-1+b1) ...
Selecting previously unselected package libxau6:armhf.
Preparing to unpack .../115-libxau6_1%3a1.0.9-1+b1_armhf.deb ...
Unpacking libxau6:armhf (1:1.0.9-1+b1) ...
Selecting previously unselected package libxdmcp6:armhf.
Preparing to unpack .../116-libxdmcp6_1%3a1.1.2-3+b1_armhf.deb ...
Unpacking libxdmcp6:armhf (1:1.1.2-3+b1) ...
Selecting previously unselected package libxcb1:armhf.
Preparing to unpack .../117-libxcb1_1.17.0-2_armhf.deb ...
Unpacking libxcb1:armhf (1.17.0-2) ...
Selecting previously unselected package libx11-data.
Preparing to unpack .../118-libx11-data_2%3a1.8.7-1_all.deb ...
Unpacking libx11-data (2:1.8.7-1) ...
Selecting previously unselected package libx11-6:armhf.
Preparing to unpack .../119-libx11-6_2%3a1.8.7-1+b1_armhf.deb ...
Unpacking libx11-6:armhf (2:1.8.7-1+b1) ...
Selecting previously unselected package libxcb-render0:armhf.
Preparing to unpack .../120-libxcb-render0_1.17.0-2_armhf.deb ...
Unpacking libxcb-render0:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-shm0:armhf.
Preparing to unpack .../121-libxcb-shm0_1.17.0-2_armhf.deb ...
Unpacking libxcb-shm0:armhf (1.17.0-2) ...
Selecting previously unselected package libxext6:armhf.
Preparing to unpack .../122-libxext6_2%3a1.3.4-1+b2_armhf.deb ...
Unpacking libxext6:armhf (2:1.3.4-1+b2) ...
Selecting previously unselected package libxrender1:armhf.
Preparing to unpack .../123-libxrender1_1%3a0.9.10-1.1+b1_armhf.deb ...
Unpacking libxrender1:armhf (1:0.9.10-1.1+b1) ...
Selecting previously unselected package libcairo2:armhf.
Preparing to unpack .../124-libcairo2_1.18.2-2_armhf.deb ...
Unpacking libcairo2:armhf (1.18.2-2) ...
Selecting previously unselected package libedit2:armhf.
Preparing to unpack .../125-libedit2_3.1-20240808-1_armhf.deb ...
Unpacking libedit2:armhf (3.1-20240808-1) ...
Selecting previously unselected package libaom3:armhf.
Preparing to unpack .../126-libaom3_3.9.1-1_armhf.deb ...
Unpacking libaom3:armhf (3.9.1-1) ...
Selecting previously unselected package libdav1d7:armhf.
Preparing to unpack .../127-libdav1d7_1.4.3-1_armhf.deb ...
Unpacking libdav1d7:armhf (1.4.3-1) ...
Selecting previously unselected package libabsl20230802:armhf.
Preparing to unpack .../128-libabsl20230802_20230802.1-4_armhf.deb ...
Unpacking libabsl20230802:armhf (20230802.1-4) ...
Selecting previously unselected package libgav1-1:armhf.
Preparing to unpack .../129-libgav1-1_0.19.0-2+b1_armhf.deb ...
Unpacking libgav1-1:armhf (0.19.0-2+b1) ...
Selecting previously unselected package librav1e0.7:armhf.
Preparing to unpack .../130-librav1e0.7_0.7.1-7+b1_armhf.deb ...
Unpacking librav1e0.7:armhf (0.7.1-7+b1) ...
Selecting previously unselected package libsvtav1enc2:armhf.
Preparing to unpack .../131-libsvtav1enc2_2.2.1+dfsg-2_armhf.deb ...
Unpacking libsvtav1enc2:armhf (2.2.1+dfsg-2) ...
Selecting previously unselected package libjpeg62-turbo:armhf.
Preparing to unpack .../132-libjpeg62-turbo_1%3a2.1.5-3_armhf.deb ...
Unpacking libjpeg62-turbo:armhf (1:2.1.5-3) ...
Selecting previously unselected package libyuv0:armhf.
Preparing to unpack .../133-libyuv0_0.0.1888.20240710-3_armhf.deb ...
Unpacking libyuv0:armhf (0.0.1888.20240710-3) ...
Selecting previously unselected package libavif16:armhf.
Preparing to unpack .../134-libavif16_1.1.1-1_armhf.deb ...
Unpacking libavif16:armhf (1.1.1-1) ...
Selecting previously unselected package libsharpyuv0:armhf.
Preparing to unpack .../135-libsharpyuv0_1.4.0-0.1_armhf.deb ...
Unpacking libsharpyuv0:armhf (1.4.0-0.1) ...
Selecting previously unselected package libheif-plugin-dav1d:armhf.
Preparing to unpack .../136-libheif-plugin-dav1d_1.18.2-2_armhf.deb ...
Unpacking libheif-plugin-dav1d:armhf (1.18.2-2) ...
Selecting previously unselected package libde265-0:armhf.
Preparing to unpack .../137-libde265-0_1.0.15-1+b2_armhf.deb ...
Unpacking libde265-0:armhf (1.0.15-1+b2) ...
Selecting previously unselected package libheif-plugin-libde265:armhf.
Preparing to unpack .../138-libheif-plugin-libde265_1.18.2-2_armhf.deb ...
Unpacking libheif-plugin-libde265:armhf (1.18.2-2) ...
Selecting previously unselected package libheif1:armhf.
Preparing to unpack .../139-libheif1_1.18.2-2_armhf.deb ...
Unpacking libheif1:armhf (1.18.2-2) ...
Selecting previously unselected package libimagequant0:armhf.
Preparing to unpack .../140-libimagequant0_2.18.0-1+b1_armhf.deb ...
Unpacking libimagequant0:armhf (2.18.0-1+b1) ...
Selecting previously unselected package libfribidi0:armhf.
Preparing to unpack .../141-libfribidi0_1.0.15-1_armhf.deb ...
Unpacking libfribidi0:armhf (1.0.15-1) ...
Selecting previously unselected package libgraphite2-3:armhf.
Preparing to unpack .../142-libgraphite2-3_1.3.14-2_armhf.deb ...
Unpacking libgraphite2-3:armhf (1.3.14-2) ...
Selecting previously unselected package libharfbuzz0b:armhf.
Preparing to unpack .../143-libharfbuzz0b_9.0.0-1_armhf.deb ...
Unpacking libharfbuzz0b:armhf (9.0.0-1) ...
Selecting previously unselected package libraqm0:armhf.
Preparing to unpack .../144-libraqm0_0.10.1-1+b1_armhf.deb ...
Unpacking libraqm0:armhf (0.10.1-1+b1) ...
Selecting previously unselected package libdeflate0:armhf.
Preparing to unpack .../145-libdeflate0_1.22-1_armhf.deb ...
Unpacking libdeflate0:armhf (1.22-1) ...
Selecting previously unselected package libjbig0:armhf.
Preparing to unpack .../146-libjbig0_2.1-6.1+b1_armhf.deb ...
Unpacking libjbig0:armhf (2.1-6.1+b1) ...
Selecting previously unselected package liblerc4:armhf.
Preparing to unpack .../147-liblerc4_4.0.0+ds-4+b1_armhf.deb ...
Unpacking liblerc4:armhf (4.0.0+ds-4+b1) ...
Selecting previously unselected package libwebp7:armhf.
Preparing to unpack .../148-libwebp7_1.4.0-0.1_armhf.deb ...
Unpacking libwebp7:armhf (1.4.0-0.1) ...
Selecting previously unselected package libtiff6:armhf.
Preparing to unpack .../149-libtiff6_4.5.1+git230720-5_armhf.deb ...
Unpacking libtiff6:armhf (4.5.1+git230720-5) ...
Selecting previously unselected package libxpm4:armhf.
Preparing to unpack .../150-libxpm4_1%3a3.5.17-1+b1_armhf.deb ...
Unpacking libxpm4:armhf (1:3.5.17-1+b1) ...
Selecting previously unselected package libgd3:armhf.
Preparing to unpack .../151-libgd3_2.3.3-12_armhf.deb ...
Unpacking libgd3:armhf (2.3.3-12) ...
Selecting previously unselected package liblua5.4-0:armhf.
Preparing to unpack .../152-liblua5.4-0_5.4.6-3+b1_armhf.deb ...
Unpacking liblua5.4-0:armhf (5.4.6-3+b1) ...
Selecting previously unselected package fontconfig.
Preparing to unpack .../153-fontconfig_2.15.0-1.1_armhf.deb ...
Unpacking fontconfig (2.15.0-1.1) ...
Selecting previously unselected package libthai-data.
Preparing to unpack .../154-libthai-data_0.1.29-2_all.deb ...
Unpacking libthai-data (0.1.29-2) ...
Selecting previously unselected package libdatrie1:armhf.
Preparing to unpack .../155-libdatrie1_0.2.13-3_armhf.deb ...
Unpacking libdatrie1:armhf (0.2.13-3) ...
Selecting previously unselected package libthai0:armhf.
Preparing to unpack .../156-libthai0_0.1.29-2_armhf.deb ...
Unpacking libthai0:armhf (0.1.29-2) ...
Selecting previously unselected package libpango-1.0-0:armhf.
Preparing to unpack .../157-libpango-1.0-0_1.54.0+ds-2_armhf.deb ...
Unpacking libpango-1.0-0:armhf (1.54.0+ds-2) ...
Selecting previously unselected package libpangoft2-1.0-0:armhf.
Preparing to unpack .../158-libpangoft2-1.0-0_1.54.0+ds-2_armhf.deb ...
Unpacking libpangoft2-1.0-0:armhf (1.54.0+ds-2) ...
Selecting previously unselected package libpangocairo-1.0-0:armhf.
Preparing to unpack .../159-libpangocairo-1.0-0_1.54.0+ds-2_armhf.deb ...
Unpacking libpangocairo-1.0-0:armhf (1.54.0+ds-2) ...
Selecting previously unselected package libwebpmux3:armhf.
Preparing to unpack .../160-libwebpmux3_1.4.0-0.1_armhf.deb ...
Unpacking libwebpmux3:armhf (1.4.0-0.1) ...
Selecting previously unselected package gnuplot-nox.
Preparing to unpack .../161-gnuplot-nox_6.0.0+dfsg1-3_armhf.deb ...
Unpacking gnuplot-nox (6.0.0+dfsg1-3) ...
Selecting previously unselected package dh-octave-autopkgtest.
Preparing to unpack .../162-dh-octave-autopkgtest_1.8.0_all.deb ...
Unpacking dh-octave-autopkgtest (1.8.0) ...
Selecting previously unselected package libapt-pkg-perl.
Preparing to unpack .../163-libapt-pkg-perl_0.1.40+b5_armhf.deb ...
Unpacking libapt-pkg-perl (0.1.40+b5) ...
Selecting previously unselected package libarray-intspan-perl.
Preparing to unpack .../164-libarray-intspan-perl_2.004-2_all.deb ...
Unpacking libarray-intspan-perl (2.004-2) ...
Selecting previously unselected package libyaml-libyaml-perl.
Preparing to unpack .../165-libyaml-libyaml-perl_0.902.0+ds-2_armhf.deb ...
Unpacking libyaml-libyaml-perl (0.902.0+ds-2) ...
Selecting previously unselected package libconfig-model-backend-yaml-perl.
Preparing to unpack .../166-libconfig-model-backend-yaml-perl_2.134-2_all.deb ...
Unpacking libconfig-model-backend-yaml-perl (2.134-2) ...
Selecting previously unselected package libexporter-lite-perl.
Preparing to unpack .../167-libexporter-lite-perl_0.09-2_all.deb ...
Unpacking libexporter-lite-perl (0.09-2) ...
Selecting previously unselected package libencode-locale-perl.
Preparing to unpack .../168-libencode-locale-perl_1.05-3_all.deb ...
Unpacking libencode-locale-perl (1.05-3) ...
Selecting previously unselected package libtimedate-perl.
Preparing to unpack .../169-libtimedate-perl_2.3300-2_all.deb ...
Unpacking libtimedate-perl (2.3300-2) ...
Selecting previously unselected package libhttp-date-perl.
Preparing to unpack .../170-libhttp-date-perl_6.06-1_all.deb ...
Unpacking libhttp-date-perl (6.06-1) ...
Selecting previously unselected package libfile-listing-perl.
Preparing to unpack .../171-libfile-listing-perl_6.16-1_all.deb ...
Unpacking libfile-listing-perl (6.16-1) ...
Selecting previously unselected package libhtml-tagset-perl.
Preparing to unpack .../172-libhtml-tagset-perl_3.24-1_all.deb ...
Unpacking libhtml-tagset-perl (3.24-1) ...
Selecting previously unselected package liburi-perl.
Preparing to unpack .../173-liburi-perl_5.30-1_all.deb ...
Unpacking liburi-perl (5.30-1) ...
Selecting previously unselected package libhtml-parser-perl:armhf.
Preparing to unpack .../174-libhtml-parser-perl_3.83-1_armhf.deb ...
Unpacking libhtml-parser-perl:armhf (3.83-1) ...
Selecting previously unselected package libhtml-tree-perl.
Preparing to unpack .../175-libhtml-tree-perl_5.07-3_all.deb ...
Unpacking libhtml-tree-perl (5.07-3) ...
Selecting previously unselected package libclone-perl:armhf.
Preparing to unpack .../176-libclone-perl_0.47-1_armhf.deb ...
Unpacking libclone-perl:armhf (0.47-1) ...
Selecting previously unselected package libio-html-perl.
Preparing to unpack .../177-libio-html-perl_1.004-3_all.deb ...
Unpacking libio-html-perl (1.004-3) ...
Selecting previously unselected package liblwp-mediatypes-perl.
Preparing to unpack .../178-liblwp-mediatypes-perl_6.04-2_all.deb ...
Unpacking liblwp-mediatypes-perl (6.04-2) ...
Selecting previously unselected package libhttp-message-perl.
Preparing to unpack .../179-libhttp-message-perl_7.00-2_all.deb ...
Unpacking libhttp-message-perl (7.00-2) ...
Selecting previously unselected package libhttp-cookies-perl.
Preparing to unpack .../180-libhttp-cookies-perl_6.11-1_all.deb ...
Unpacking libhttp-cookies-perl (6.11-1) ...
Selecting previously unselected package libhttp-negotiate-perl.
Preparing to unpack .../181-libhttp-negotiate-perl_6.01-2_all.deb ...
Unpacking libhttp-negotiate-perl (6.01-2) ...
Selecting previously unselected package perl-openssl-defaults:armhf.
Preparing to unpack .../182-perl-openssl-defaults_7+b2_armhf.deb ...
Unpacking perl-openssl-defaults:armhf (7+b2) ...
Selecting previously unselected package libnet-ssleay-perl:armhf.
Preparing to unpack .../183-libnet-ssleay-perl_1.94-1+b1_armhf.deb ...
Unpacking libnet-ssleay-perl:armhf (1.94-1+b1) ...
Selecting previously unselected package libio-socket-ssl-perl.
Preparing to unpack .../184-libio-socket-ssl-perl_2.089-1_all.deb ...
Unpacking libio-socket-ssl-perl (2.089-1) ...
Selecting previously unselected package libnet-http-perl.
Preparing to unpack .../185-libnet-http-perl_6.23-1_all.deb ...
Unpacking libnet-http-perl (6.23-1) ...
Selecting previously unselected package liblwp-protocol-https-perl.
Preparing to unpack .../186-liblwp-protocol-https-perl_6.14-1_all.deb ...
Unpacking liblwp-protocol-https-perl (6.14-1) ...
Selecting previously unselected package libwww-robotrules-perl.
Preparing to unpack .../187-libwww-robotrules-perl_6.02-1_all.deb ...
Unpacking libwww-robotrules-perl (6.02-1) ...
Selecting previously unselected package libwww-perl.
Preparing to unpack .../188-libwww-perl_6.77-1_all.deb ...
Unpacking libwww-perl (6.77-1) ...
Selecting previously unselected package liberror-perl.
Preparing to unpack .../189-liberror-perl_0.17029-2_all.deb ...
Unpacking liberror-perl (0.17029-2) ...
Selecting previously unselected package libparse-debcontrol-perl.
Preparing to unpack .../190-libparse-debcontrol-perl_2.005-6_all.deb ...
Unpacking libparse-debcontrol-perl (2.005-6) ...
Selecting previously unselected package libsoftware-copyright-perl.
Preparing to unpack .../191-libsoftware-copyright-perl_0.012-2_all.deb ...
Unpacking libsoftware-copyright-perl (0.012-2) ...
Selecting previously unselected package libalgorithm-c3-perl.
Preparing to unpack .../192-libalgorithm-c3-perl_0.11-2_all.deb ...
Unpacking libalgorithm-c3-perl (0.11-2) ...
Selecting previously unselected package libclass-c3-perl.
Preparing to unpack .../193-libclass-c3-perl_0.35-2_all.deb ...
Unpacking libclass-c3-perl (0.35-2) ...
Selecting previously unselected package libmro-compat-perl.
Preparing to unpack .../194-libmro-compat-perl_0.15-2_all.deb ...
Unpacking libmro-compat-perl (0.15-2) ...
Selecting previously unselected package libdata-section-perl.
Preparing to unpack .../195-libdata-section-perl_0.200008-1_all.deb ...
Unpacking libdata-section-perl (0.200008-1) ...
Selecting previously unselected package libtext-template-perl.
Preparing to unpack .../196-libtext-template-perl_1.61-1_all.deb ...
Unpacking libtext-template-perl (1.61-1) ...
Selecting previously unselected package libsoftware-license-perl.
Preparing to unpack .../197-libsoftware-license-perl_0.104006-1_all.deb ...
Unpacking libsoftware-license-perl (0.104006-1) ...
Selecting previously unselected package libsoftware-licensemoreutils-perl.
Preparing to unpack .../198-libsoftware-licensemoreutils-perl_1.009-1_all.deb ...
Unpacking libsoftware-licensemoreutils-perl (1.009-1) ...
Selecting previously unselected package libsort-versions-perl.
Preparing to unpack .../199-libsort-versions-perl_1.62-3_all.deb ...
Unpacking libsort-versions-perl (1.62-3) ...
Selecting previously unselected package libtext-reform-perl.
Preparing to unpack .../200-libtext-reform-perl_1.20-5_all.deb ...
Unpacking libtext-reform-perl (1.20-5) ...
Selecting previously unselected package libtext-autoformat-perl.
Preparing to unpack .../201-libtext-autoformat-perl_1.750000-2_all.deb ...
Unpacking libtext-autoformat-perl (1.750000-2) ...
Selecting previously unselected package libtext-levenshtein-damerau-perl.
Preparing to unpack .../202-libtext-levenshtein-damerau-perl_0.41-3_all.deb ...
Unpacking libtext-levenshtein-damerau-perl (0.41-3) ...
Selecting previously unselected package libtoml-tiny-perl.
Preparing to unpack .../203-libtoml-tiny-perl_0.18-1_all.deb ...
Unpacking libtoml-tiny-perl (0.18-1) ...
Selecting previously unselected package libclass-inspector-perl.
Preparing to unpack .../204-libclass-inspector-perl_1.36-3_all.deb ...
Unpacking libclass-inspector-perl (1.36-3) ...
Selecting previously unselected package libfile-sharedir-perl.
Preparing to unpack .../205-libfile-sharedir-perl_1.118-3_all.deb ...
Unpacking libfile-sharedir-perl (1.118-3) ...
Selecting previously unselected package libindirect-perl.
Preparing to unpack .../206-libindirect-perl_0.39-2+b3_armhf.deb ...
Unpacking libindirect-perl (0.39-2+b3) ...
Selecting previously unselected package libxs-parse-keyword-perl.
Preparing to unpack .../207-libxs-parse-keyword-perl_0.46-1_armhf.deb ...
Unpacking libxs-parse-keyword-perl (0.46-1) ...
Selecting previously unselected package libxs-parse-sublike-perl:armhf.
Preparing to unpack .../208-libxs-parse-sublike-perl_0.28-1_armhf.deb ...
Unpacking libxs-parse-sublike-perl:armhf (0.28-1) ...
Selecting previously unselected package libobject-pad-perl.
Preparing to unpack .../209-libobject-pad-perl_0.814-1_armhf.deb ...
Unpacking libobject-pad-perl (0.814-1) ...
Selecting previously unselected package libfeature-compat-class-perl.
Preparing to unpack .../210-libfeature-compat-class-perl_0.07-1_all.deb ...
Unpacking libfeature-compat-class-perl (0.07-1) ...
Selecting previously unselected package libsyntax-keyword-try-perl.
Preparing to unpack .../211-libsyntax-keyword-try-perl_0.30-1_armhf.deb ...
Unpacking libsyntax-keyword-try-perl (0.30-1) ...
Selecting previously unselected package libfeature-compat-try-perl.
Preparing to unpack .../212-libfeature-compat-try-perl_0.05-1_all.deb ...
Unpacking libfeature-compat-try-perl (0.05-1) ...
Selecting previously unselected package libio-interactive-perl.
Preparing to unpack .../213-libio-interactive-perl_1.025-1_all.deb ...
Unpacking libio-interactive-perl (1.025-1) ...
Selecting previously unselected package liblog-any-perl.
Preparing to unpack .../214-liblog-any-perl_1.717-1_all.deb ...
Unpacking liblog-any-perl (1.717-1) ...
Selecting previously unselected package liblog-any-adapter-screen-perl.
Preparing to unpack .../215-liblog-any-adapter-screen-perl_0.141-1_all.deb ...
Unpacking liblog-any-adapter-screen-perl (0.141-1) ...
Selecting previously unselected package libsub-exporter-progressive-perl.
Preparing to unpack .../216-libsub-exporter-progressive-perl_0.001013-3_all.deb ...
Unpacking libsub-exporter-progressive-perl (0.001013-3) ...
Selecting previously unselected package libvariable-magic-perl.
Preparing to unpack .../217-libvariable-magic-perl_0.64-1_armhf.deb ...
Unpacking libvariable-magic-perl (0.64-1) ...
Selecting previously unselected package libb-hooks-endofscope-perl.
Preparing to unpack .../218-libb-hooks-endofscope-perl_0.28-1_all.deb ...
Unpacking libb-hooks-endofscope-perl (0.28-1) ...
Selecting previously unselected package libsub-identify-perl.
Preparing to unpack .../219-libsub-identify-perl_0.14-3+b2_armhf.deb ...
Unpacking libsub-identify-perl (0.14-3+b2) ...
Selecting previously unselected package libsub-name-perl:armhf.
Preparing to unpack .../220-libsub-name-perl_0.27-1+b2_armhf.deb ...
Unpacking libsub-name-perl:armhf (0.27-1+b2) ...
Selecting previously unselected package libnamespace-clean-perl.
Preparing to unpack .../221-libnamespace-clean-perl_0.27-2_all.deb ...
Unpacking libnamespace-clean-perl (0.27-2) ...
Selecting previously unselected package libnumber-compare-perl.
Preparing to unpack .../222-libnumber-compare-perl_0.03-3_all.deb ...
Unpacking libnumber-compare-perl (0.03-3) ...
Selecting previously unselected package libtext-glob-perl.
Preparing to unpack .../223-libtext-glob-perl_0.11-3_all.deb ...
Unpacking libtext-glob-perl (0.11-3) ...
Selecting previously unselected package libpath-iterator-rule-perl.
Preparing to unpack .../224-libpath-iterator-rule-perl_1.015-2_all.deb ...
Unpacking libpath-iterator-rule-perl (1.015-2) ...
Selecting previously unselected package libpod-parser-perl.
Preparing to unpack .../225-libpod-parser-perl_1.67-1_all.deb ...
Adding 'diversion of /usr/bin/podselect to /usr/bin/podselect.bundled by libpod-parser-perl'
Adding 'diversion of /usr/share/man/man1/podselect.1.gz to /usr/share/man/man1/podselect.bundled.1.gz by libpod-parser-perl'
Unpacking libpod-parser-perl (1.67-1) ...
Selecting previously unselected package libpod-constants-perl.
Preparing to unpack .../226-libpod-constants-perl_0.19-2_all.deb ...
Unpacking libpod-constants-perl (0.19-2) ...
Selecting previously unselected package libset-intspan-perl.
Preparing to unpack .../227-libset-intspan-perl_1.19-3_all.deb ...
Unpacking libset-intspan-perl (1.19-3) ...
Selecting previously unselected package libstring-copyright-perl.
Preparing to unpack .../228-libstring-copyright-perl_0.003014-1_all.deb ...
Unpacking libstring-copyright-perl (0.003014-1) ...
Selecting previously unselected package libstring-escape-perl.
Preparing to unpack .../229-libstring-escape-perl_2010.002-3_all.deb ...
Unpacking libstring-escape-perl (2010.002-3) ...
Selecting previously unselected package libregexp-pattern-license-perl.
Preparing to unpack .../230-libregexp-pattern-license-perl_3.11.2-1_all.deb ...
Unpacking libregexp-pattern-license-perl (3.11.2-1) ...
Selecting previously unselected package libregexp-pattern-perl.
Preparing to unpack .../231-libregexp-pattern-perl_0.2.14-2_all.deb ...
Unpacking libregexp-pattern-perl (0.2.14-2) ...
Selecting previously unselected package libstring-license-perl.
Preparing to unpack .../232-libstring-license-perl_0.0.11-1_all.deb ...
Unpacking libstring-license-perl (0.0.11-1) ...
Selecting previously unselected package licensecheck.
Preparing to unpack .../233-licensecheck_3.3.9-1_all.deb ...
Unpacking licensecheck (3.3.9-1) ...
Selecting previously unselected package diffstat.
Preparing to unpack .../234-diffstat_1.66-1_armhf.deb ...
Unpacking diffstat (1.66-1) ...
Selecting previously unselected package libassuan9:armhf.
Preparing to unpack .../235-libassuan9_3.0.1-2_armhf.deb ...
Unpacking libassuan9:armhf (3.0.1-2) ...
Selecting previously unselected package libreadline8t64:armhf.
Preparing to unpack .../236-libreadline8t64_8.2-5_armhf.deb ...
Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8 to /lib/arm-linux-gnueabihf/libhistory.so.8.usr-is-merged by libreadline8t64'
Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8.2 to /lib/arm-linux-gnueabihf/libhistory.so.8.2.usr-is-merged by libreadline8t64'
Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8 to /lib/arm-linux-gnueabihf/libreadline.so.8.usr-is-merged by libreadline8t64'
Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8.2 to /lib/arm-linux-gnueabihf/libreadline.so.8.2.usr-is-merged by libreadline8t64'
Unpacking libreadline8t64:armhf (8.2-5) ...
Selecting previously unselected package gpgconf.
Preparing to unpack .../237-gpgconf_2.2.44-1_armhf.deb ...
Unpacking gpgconf (2.2.44-1) ...
Selecting previously unselected package gpg.
Preparing to unpack .../238-gpg_2.2.44-1_armhf.deb ...
Unpacking gpg (2.2.44-1) ...
Selecting previously unselected package iso-codes.
Preparing to unpack .../239-iso-codes_4.17.0-1_all.deb ...
Unpacking iso-codes (4.17.0-1) ...
Selecting previously unselected package libberkeleydb-perl:armhf.
Preparing to unpack .../240-libberkeleydb-perl_0.64-2+b3_armhf.deb ...
Unpacking libberkeleydb-perl:armhf (0.64-2+b3) ...
Selecting previously unselected package libclass-xsaccessor-perl.
Preparing to unpack .../241-libclass-xsaccessor-perl_1.19-4+b3_armhf.deb ...
Unpacking libclass-xsaccessor-perl (1.19-4+b3) ...
Selecting previously unselected package libconfig-tiny-perl.
Preparing to unpack .../242-libconfig-tiny-perl_2.30-1_all.deb ...
Unpacking libconfig-tiny-perl (2.30-1) ...
Selecting previously unselected package libconst-fast-perl.
Preparing to unpack .../243-libconst-fast-perl_0.014-2_all.deb ...
Unpacking libconst-fast-perl (0.014-2) ...
Selecting previously unselected package libcpanel-json-xs-perl:armhf.
Preparing to unpack .../244-libcpanel-json-xs-perl_4.38-1_armhf.deb ...
Unpacking libcpanel-json-xs-perl:armhf (4.38-1) ...
Selecting previously unselected package libaliased-perl.
Preparing to unpack .../245-libaliased-perl_0.34-3_all.deb ...
Unpacking libaliased-perl (0.34-3) ...
Selecting previously unselected package libclass-data-inheritable-perl.
Preparing to unpack .../246-libclass-data-inheritable-perl_0.08-3_all.deb ...
Unpacking libclass-data-inheritable-perl (0.08-3) ...
Selecting previously unselected package libdevel-stacktrace-perl.
Preparing to unpack .../247-libdevel-stacktrace-perl_2.0500-1_all.deb ...
Unpacking libdevel-stacktrace-perl (2.0500-1) ...
Selecting previously unselected package libexception-class-perl.
Preparing to unpack .../248-libexception-class-perl_1.45-1_all.deb ...
Unpacking libexception-class-perl (1.45-1) ...
Selecting previously unselected package libiterator-perl.
Preparing to unpack .../249-libiterator-perl_0.03+ds1-2_all.deb ...
Unpacking libiterator-perl (0.03+ds1-2) ...
Selecting previously unselected package libiterator-util-perl.
Preparing to unpack .../250-libiterator-util-perl_0.02+ds1-2_all.deb ...
Unpacking libiterator-util-perl (0.02+ds1-2) ...
Selecting previously unselected package libdata-dpath-perl.
Preparing to unpack .../251-libdata-dpath-perl_0.60-1_all.deb ...
Unpacking libdata-dpath-perl (0.60-1) ...
Selecting previously unselected package libnet-domain-tld-perl.
Preparing to unpack .../252-libnet-domain-tld-perl_1.75-4_all.deb ...
Unpacking libnet-domain-tld-perl (1.75-4) ...
Selecting previously unselected package libdata-validate-domain-perl.
Preparing to unpack .../253-libdata-validate-domain-perl_0.15-1_all.deb ...
Unpacking libdata-validate-domain-perl (0.15-1) ...
Selecting previously unselected package libnet-ipv6addr-perl.
Preparing to unpack .../254-libnet-ipv6addr-perl_1.02-1_all.deb ...
Unpacking libnet-ipv6addr-perl (1.02-1) ...
Selecting previously unselected package libnet-netmask-perl.
Preparing to unpack .../255-libnet-netmask-perl_2.0002-2_all.deb ...
Unpacking libnet-netmask-perl (2.0002-2) ...
Selecting previously unselected package libnetaddr-ip-perl.
Preparing to unpack .../256-libnetaddr-ip-perl_4.079+dfsg-2+b3_armhf.deb ...
Unpacking libnetaddr-ip-perl (4.079+dfsg-2+b3) ...
Selecting previously unselected package libdata-validate-ip-perl.
Preparing to unpack .../257-libdata-validate-ip-perl_0.31-1_all.deb ...
Unpacking libdata-validate-ip-perl (0.31-1) ...
Selecting previously unselected package libdata-validate-uri-perl.
Preparing to unpack .../258-libdata-validate-uri-perl_0.07-3_all.deb ...
Unpacking libdata-validate-uri-perl (0.07-3) ...
Selecting previously unselected package libdevel-size-perl.
Preparing to unpack .../259-libdevel-size-perl_0.84-1_armhf.deb ...
Unpacking libdevel-size-perl (0.84-1) ...
Selecting previously unselected package libemail-address-xs-perl.
Preparing to unpack .../260-libemail-address-xs-perl_1.05-1+b3_armhf.deb ...
Unpacking libemail-address-xs-perl (1.05-1+b3) ...
Selecting previously unselected package libipc-system-simple-perl.
Preparing to unpack .../261-libipc-system-simple-perl_1.30-2_all.deb ...
Unpacking libipc-system-simple-perl (1.30-2) ...
Selecting previously unselected package libfile-basedir-perl.
Preparing to unpack .../262-libfile-basedir-perl_0.09-2_all.deb ...
Unpacking libfile-basedir-perl (0.09-2) ...
Selecting previously unselected package libfile-find-rule-perl.
Preparing to unpack .../263-libfile-find-rule-perl_0.34-3_all.deb ...
Unpacking libfile-find-rule-perl (0.34-3) ...
Selecting previously unselected package libio-string-perl.
Preparing to unpack .../264-libio-string-perl_1.08-4_all.deb ...
Unpacking libio-string-perl (1.08-4) ...
Selecting previously unselected package libfont-ttf-perl.
Preparing to unpack .../265-libfont-ttf-perl_1.06-2_all.deb ...
Unpacking libfont-ttf-perl (1.06-2) ...
Selecting previously unselected package libhtml-html5-entities-perl.
Preparing to unpack .../266-libhtml-html5-entities-perl_0.004-3_all.deb ...
Unpacking libhtml-html5-entities-perl (0.004-3) ...
Selecting previously unselected package libhtml-tokeparser-simple-perl.
Preparing to unpack .../267-libhtml-tokeparser-simple-perl_3.16-4_all.deb ...
Unpacking libhtml-tokeparser-simple-perl (3.16-4) ...
Selecting previously unselected package libipc-run3-perl.
Preparing to unpack .../268-libipc-run3-perl_0.049-1_all.deb ...
Unpacking libipc-run3-perl (0.049-1) ...
Selecting previously unselected package libjson-maybexs-perl.
Preparing to unpack .../269-libjson-maybexs-perl_1.004008-1_all.deb ...
Unpacking libjson-maybexs-perl (1.004008-1) ...
Selecting previously unselected package liblist-compare-perl.
Preparing to unpack .../270-liblist-compare-perl_0.55-2_all.deb ...
Unpacking liblist-compare-perl (0.55-2) ...
Selecting previously unselected package liblist-someutils-perl.
Preparing to unpack .../271-liblist-someutils-perl_0.59-1_all.deb ...
Unpacking liblist-someutils-perl (0.59-1) ...
Selecting previously unselected package liblist-utilsby-perl.
Preparing to unpack .../272-liblist-utilsby-perl_0.12-2_all.deb ...
Unpacking liblist-utilsby-perl (0.12-2) ...
Selecting previously unselected package libmldbm-perl.
Preparing to unpack .../273-libmldbm-perl_2.05-4_all.deb ...
Unpacking libmldbm-perl (2.05-4) ...
Selecting previously unselected package libclass-method-modifiers-perl.
Preparing to unpack .../274-libclass-method-modifiers-perl_2.15-1_all.deb ...
Unpacking libclass-method-modifiers-perl (2.15-1) ...
Selecting previously unselected package libimport-into-perl.
Preparing to unpack .../275-libimport-into-perl_1.002005-2_all.deb ...
Unpacking libimport-into-perl (1.002005-2) ...
Selecting previously unselected package librole-tiny-perl.
Preparing to unpack .../276-librole-tiny-perl_2.002004-1_all.deb ...
Unpacking librole-tiny-perl (2.002004-1) ...
Selecting previously unselected package libsub-quote-perl.
Preparing to unpack .../277-libsub-quote-perl_2.006008-1_all.deb ...
Unpacking libsub-quote-perl (2.006008-1) ...
Selecting previously unselected package libmoo-perl.
Preparing to unpack .../278-libmoo-perl_2.005005-1_all.deb ...
Unpacking libmoo-perl (2.005005-1) ...
Selecting previously unselected package libstrictures-perl.
Preparing to unpack .../279-libstrictures-perl_2.000006-1_all.deb ...
Unpacking libstrictures-perl (2.000006-1) ...
Selecting previously unselected package libmoox-aliases-perl.
Preparing to unpack .../280-libmoox-aliases-perl_0.001006-2_all.deb ...
Unpacking libmoox-aliases-perl (0.001006-2) ...
Selecting previously unselected package libperlio-gzip-perl.
Preparing to unpack .../281-libperlio-gzip-perl_0.20-1+b3_armhf.deb ...
Unpacking libperlio-gzip-perl (0.20-1+b3) ...
Selecting previously unselected package libperlio-utf8-strict-perl.
Preparing to unpack .../282-libperlio-utf8-strict-perl_0.010-1+b2_armhf.deb ...
Unpacking libperlio-utf8-strict-perl (0.010-1+b2) ...
Selecting previously unselected package libproc-processtable-perl:armhf.
Preparing to unpack .../283-libproc-processtable-perl_0.636-1+b2_armhf.deb ...
Unpacking libproc-processtable-perl:armhf (0.636-1+b2) ...
Selecting previously unselected package libregexp-wildcards-perl.
Preparing to unpack .../284-libregexp-wildcards-perl_1.05-3_all.deb ...
Unpacking libregexp-wildcards-perl (1.05-3) ...
Selecting previously unselected package libsereal-decoder-perl.
Preparing to unpack .../285-libsereal-decoder-perl_5.004+ds-1+b2_armhf.deb ...
Unpacking libsereal-decoder-perl (5.004+ds-1+b2) ...
Selecting previously unselected package libsereal-encoder-perl.
Preparing to unpack .../286-libsereal-encoder-perl_5.004+ds-1+b2_armhf.deb ...
Unpacking libsereal-encoder-perl (5.004+ds-1+b2) ...
Selecting previously unselected package libterm-readkey-perl.
Preparing to unpack .../287-libterm-readkey-perl_2.38-2+b3_armhf.deb ...
Unpacking libterm-readkey-perl (2.38-2+b3) ...
Selecting previously unselected package libtext-levenshteinxs-perl.
Preparing to unpack .../288-libtext-levenshteinxs-perl_0.03-5+b3_armhf.deb ...
Unpacking libtext-levenshteinxs-perl (0.03-5+b3) ...
Selecting previously unselected package libmarkdown2:armhf.
Preparing to unpack .../289-libmarkdown2_2.2.7-2+b1_armhf.deb ...
Unpacking libmarkdown2:armhf (2.2.7-2+b1) ...
Selecting previously unselected package libtext-markdown-discount-perl.
Preparing to unpack .../290-libtext-markdown-discount-perl_0.16-1+b2_armhf.deb ...
Unpacking libtext-markdown-discount-perl (0.16-1+b2) ...
Selecting previously unselected package libdata-messagepack-perl.
Preparing to unpack .../291-libdata-messagepack-perl_1.02-1+b3_armhf.deb ...
Unpacking libdata-messagepack-perl (1.02-1+b3) ...
Selecting previously unselected package libtext-xslate-perl:armhf.
Preparing to unpack .../292-libtext-xslate-perl_3.5.9-2_armhf.deb ...
Unpacking libtext-xslate-perl:armhf (3.5.9-2) ...
Selecting previously unselected package libtime-duration-perl.
Preparing to unpack .../293-libtime-duration-perl_1.21-2_all.deb ...
Unpacking libtime-duration-perl (1.21-2) ...
Selecting previously unselected package libtime-moment-perl.
Preparing to unpack .../294-libtime-moment-perl_0.44-2+b3_armhf.deb ...
Unpacking libtime-moment-perl (0.44-2+b3) ...
Selecting previously unselected package libunicode-utf8-perl.
Preparing to unpack .../295-libunicode-utf8-perl_0.62-2+b2_armhf.deb ...
Unpacking libunicode-utf8-perl (0.62-2+b2) ...
Selecting previously unselected package libcgi-pm-perl.
Preparing to unpack .../296-libcgi-pm-perl_4.66-1_all.deb ...
Unpacking libcgi-pm-perl (4.66-1) ...
Selecting previously unselected package libhtml-form-perl.
Preparing to unpack .../297-libhtml-form-perl_6.11-2_all.deb ...
Unpacking libhtml-form-perl (6.11-2) ...
Selecting previously unselected package libwww-mechanize-perl.
Preparing to unpack .../298-libwww-mechanize-perl_2.19-1_all.deb ...
Unpacking libwww-mechanize-perl (2.19-1) ...
Selecting previously unselected package libxml-namespacesupport-perl.
Preparing to unpack .../299-libxml-namespacesupport-perl_1.12-2_all.deb ...
Unpacking libxml-namespacesupport-perl (1.12-2) ...
Selecting previously unselected package libxml-sax-base-perl.
Preparing to unpack .../300-libxml-sax-base-perl_1.09-3_all.deb ...
Unpacking libxml-sax-base-perl (1.09-3) ...
Selecting previously unselected package libxml-sax-perl.
Preparing to unpack .../301-libxml-sax-perl_1.02+dfsg-3_all.deb ...
Unpacking libxml-sax-perl (1.02+dfsg-3) ...
Selecting previously unselected package libxml-libxml-perl.
Preparing to unpack .../302-libxml-libxml-perl_2.0207+dfsg+really+2.0134-5_armhf.deb ...
Unpacking libxml-libxml-perl (2.0207+dfsg+really+2.0134-5) ...
Selecting previously unselected package liblz1:armhf.
Preparing to unpack .../303-liblz1_1.15~pre1-1_armhf.deb ...
Unpacking liblz1:armhf (1.15~pre1-1) ...
Selecting previously unselected package plzip.
Preparing to unpack .../304-plzip_1.11-2_armhf.deb ...
Unpacking plzip (1.11-2) ...
Selecting previously unselected package liblzo2-2:armhf.
Preparing to unpack .../305-liblzo2-2_2.10-3_armhf.deb ...
Unpacking liblzo2-2:armhf (2.10-3) ...
Selecting previously unselected package lzop.
Preparing to unpack .../306-lzop_1.04-2_armhf.deb ...
Unpacking lzop (1.04-2) ...
Selecting previously unselected package patchutils.
Preparing to unpack .../307-patchutils_0.4.2-1_armhf.deb ...
Unpacking patchutils (0.4.2-1) ...
Selecting previously unselected package t1utils.
Preparing to unpack .../308-t1utils_1.41-4_armhf.deb ...
Unpacking t1utils (1.41-4) ...
Selecting previously unselected package unzip.
Preparing to unpack .../309-unzip_6.0-28_armhf.deb ...
Unpacking unzip (6.0-28) ...
Selecting previously unselected package lintian.
Preparing to unpack .../310-lintian_2.119.0_all.deb ...
Unpacking lintian (2.119.0) ...
Selecting previously unselected package libconfig-model-dpkg-perl.
Preparing to unpack .../311-libconfig-model-dpkg-perl_3.005_all.deb ...
Unpacking libconfig-model-dpkg-perl (3.005) ...
Selecting previously unselected package libconvert-binhex-perl.
Preparing to unpack .../312-libconvert-binhex-perl_1.125-3_all.deb ...
Unpacking libconvert-binhex-perl (1.125-3) ...
Selecting previously unselected package libnet-smtp-ssl-perl.
Preparing to unpack .../313-libnet-smtp-ssl-perl_1.04-2_all.deb ...
Unpacking libnet-smtp-ssl-perl (1.04-2) ...
Selecting previously unselected package libmailtools-perl.
Preparing to unpack .../314-libmailtools-perl_2.21-3_all.deb ...
Unpacking libmailtools-perl (2.21-3) ...
Selecting previously unselected package libmime-tools-perl.
Preparing to unpack .../315-libmime-tools-perl_5.515-1_all.deb ...
Unpacking libmime-tools-perl (5.515-1) ...
Selecting previously unselected package libsuitesparseconfig7:armhf.
Preparing to unpack .../316-libsuitesparseconfig7_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libsuitesparseconfig7:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libamd3:armhf.
Preparing to unpack .../317-libamd3_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libamd3:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libblas3:armhf.
Preparing to unpack .../318-libblas3_3.12.0-3_armhf.deb ...
Unpacking libblas3:armhf (3.12.0-3) ...
Selecting previously unselected package libgfortran5:armhf.
Preparing to unpack .../319-libgfortran5_14.2.0-6_armhf.deb ...
Unpacking libgfortran5:armhf (14.2.0-6) ...
Selecting previously unselected package liblapack3:armhf.
Preparing to unpack .../320-liblapack3_3.12.0-3_armhf.deb ...
Unpacking liblapack3:armhf (3.12.0-3) ...
Selecting previously unselected package libarpack2t64:armhf.
Preparing to unpack .../321-libarpack2t64_3.9.1-1.1+b1_armhf.deb ...
Unpacking libarpack2t64:armhf (3.9.1-1.1+b1) ...
Selecting previously unselected package libccolamd3:armhf.
Preparing to unpack .../322-libccolamd3_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libccolamd3:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libcamd3:armhf.
Preparing to unpack .../323-libcamd3_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libcamd3:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libcolamd3:armhf.
Preparing to unpack .../324-libcolamd3_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libcolamd3:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libcholmod5:armhf.
Preparing to unpack .../325-libcholmod5_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libcholmod5:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libcxsparse4:armhf.
Preparing to unpack .../326-libcxsparse4_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libcxsparse4:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libfftw3-double3:armhf.
Preparing to unpack .../327-libfftw3-double3_3.3.10-1+b3_armhf.deb ...
Unpacking libfftw3-double3:armhf (3.3.10-1+b3) ...
Selecting previously unselected package libfftw3-single3:armhf.
Preparing to unpack .../328-libfftw3-single3_3.3.10-1+b3_armhf.deb ...
Unpacking libfftw3-single3:armhf (3.3.10-1+b3) ...
Selecting previously unselected package libxfixes3:armhf.
Preparing to unpack .../329-libxfixes3_1%3a6.0.0-2+b1_armhf.deb ...
Unpacking libxfixes3:armhf (1:6.0.0-2+b1) ...
Selecting previously unselected package libxcursor1:armhf.
Preparing to unpack .../330-libxcursor1_1%3a1.2.2-1_armhf.deb ...
Unpacking libxcursor1:armhf (1:1.2.2-1) ...
Selecting previously unselected package libxft2:armhf.
Preparing to unpack .../331-libxft2_2.3.6-1+b1_armhf.deb ...
Unpacking libxft2:armhf (2.3.6-1+b1) ...
Selecting previously unselected package libxinerama1:armhf.
Preparing to unpack .../332-libxinerama1_2%3a1.1.4-3+b1_armhf.deb ...
Unpacking libxinerama1:armhf (2:1.1.4-3+b1) ...
Selecting previously unselected package libfltk1.3t64:armhf.
Preparing to unpack .../333-libfltk1.3t64_1.3.8-6.1_armhf.deb ...
Unpacking libfltk1.3t64:armhf (1.3.8-6.1) ...
Selecting previously unselected package libglvnd0:armhf.
Preparing to unpack .../334-libglvnd0_1.7.0-1+b1_armhf.deb ...
Unpacking libglvnd0:armhf (1.7.0-1+b1) ...
Selecting previously unselected package libdrm-common.
Preparing to unpack .../335-libdrm-common_2.4.123-1_all.deb ...
Unpacking libdrm-common (2.4.123-1) ...
Selecting previously unselected package libdrm2:armhf.
Preparing to unpack .../336-libdrm2_2.4.123-1_armhf.deb ...
Unpacking libdrm2:armhf (2.4.123-1) ...
Selecting previously unselected package libglapi-mesa:armhf.
Preparing to unpack .../337-libglapi-mesa_24.2.4-1_armhf.deb ...
Unpacking libglapi-mesa:armhf (24.2.4-1) ...
Selecting previously unselected package libx11-xcb1:armhf.
Preparing to unpack .../338-libx11-xcb1_2%3a1.8.7-1+b1_armhf.deb ...
Unpacking libx11-xcb1:armhf (2:1.8.7-1+b1) ...
Selecting previously unselected package libxcb-dri2-0:armhf.
Preparing to unpack .../339-libxcb-dri2-0_1.17.0-2_armhf.deb ...
Unpacking libxcb-dri2-0:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-dri3-0:armhf.
Preparing to unpack .../340-libxcb-dri3-0_1.17.0-2_armhf.deb ...
Unpacking libxcb-dri3-0:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-glx0:armhf.
Preparing to unpack .../341-libxcb-glx0_1.17.0-2_armhf.deb ...
Unpacking libxcb-glx0:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-present0:armhf.
Preparing to unpack .../342-libxcb-present0_1.17.0-2_armhf.deb ...
Unpacking libxcb-present0:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-randr0:armhf.
Preparing to unpack .../343-libxcb-randr0_1.17.0-2_armhf.deb ...
Unpacking libxcb-randr0:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-sync1:armhf.
Preparing to unpack .../344-libxcb-sync1_1.17.0-2_armhf.deb ...
Unpacking libxcb-sync1:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-xfixes0:armhf.
Preparing to unpack .../345-libxcb-xfixes0_1.17.0-2_armhf.deb ...
Unpacking libxcb-xfixes0:armhf (1.17.0-2) ...
Selecting previously unselected package libxshmfence1:armhf.
Preparing to unpack .../346-libxshmfence1_1.3-1+b1_armhf.deb ...
Unpacking libxshmfence1:armhf (1.3-1+b1) ...
Selecting previously unselected package libxxf86vm1:armhf.
Preparing to unpack .../347-libxxf86vm1_1%3a1.1.4-1+b3_armhf.deb ...
Unpacking libxxf86vm1:armhf (1:1.1.4-1+b3) ...
Selecting previously unselected package libdrm-amdgpu1:armhf.
Preparing to unpack .../348-libdrm-amdgpu1_2.4.123-1_armhf.deb ...
Unpacking libdrm-amdgpu1:armhf (2.4.123-1) ...
Selecting previously unselected package libdrm-radeon1:armhf.
Preparing to unpack .../349-libdrm-radeon1_2.4.123-1_armhf.deb ...
Unpacking libdrm-radeon1:armhf (2.4.123-1) ...
Selecting previously unselected package libz3-4:armhf.
Preparing to unpack .../350-libz3-4_4.8.12-3.1+b2_armhf.deb ...
Unpacking libz3-4:armhf (4.8.12-3.1+b2) ...
Selecting previously unselected package libllvm19:armhf.
Preparing to unpack .../351-libllvm19_1%3a19.1.1-1_armhf.deb ...
Unpacking libllvm19:armhf (1:19.1.1-1) ...
Selecting previously unselected package libsensors-config.
Preparing to unpack .../352-libsensors-config_1%3a3.6.0-10_all.deb ...
Unpacking libsensors-config (1:3.6.0-10) ...
Selecting previously unselected package libsensors5:armhf.
Preparing to unpack .../353-libsensors5_1%3a3.6.0-10_armhf.deb ...
Unpacking libsensors5:armhf (1:3.6.0-10) ...
Selecting previously unselected package mesa-libgallium:armhf.
Preparing to unpack .../354-mesa-libgallium_24.2.4-1_armhf.deb ...
Unpacking mesa-libgallium:armhf (24.2.4-1) ...
Selecting previously unselected package libvulkan1:armhf.
Preparing to unpack .../355-libvulkan1_1.3.290.0-1_armhf.deb ...
Unpacking libvulkan1:armhf (1.3.290.0-1) ...
Selecting previously unselected package libwayland-server0:armhf.
Preparing to unpack .../356-libwayland-server0_1.23.0-1_armhf.deb ...
Unpacking libwayland-server0:armhf (1.23.0-1) ...
Selecting previously unselected package libgbm1:armhf.
Preparing to unpack .../357-libgbm1_24.2.4-1_armhf.deb ...
Unpacking libgbm1:armhf (24.2.4-1) ...
Selecting previously unselected package libgl1-mesa-dri:armhf.
Preparing to unpack .../358-libgl1-mesa-dri_24.2.4-1_armhf.deb ...
Unpacking libgl1-mesa-dri:armhf (24.2.4-1) ...
Selecting previously unselected package libglx-mesa0:armhf.
Preparing to unpack .../359-libglx-mesa0_24.2.4-1_armhf.deb ...
Unpacking libglx-mesa0:armhf (24.2.4-1) ...
Selecting previously unselected package libglx0:armhf.
Preparing to unpack .../360-libglx0_1.7.0-1+b1_armhf.deb ...
Unpacking libglx0:armhf (1.7.0-1+b1) ...
Selecting previously unselected package libgl1:armhf.
Preparing to unpack .../361-libgl1_1.7.0-1+b1_armhf.deb ...
Unpacking libgl1:armhf (1.7.0-1+b1) ...
Selecting previously unselected package libfltk-gl1.3t64:armhf.
Preparing to unpack .../362-libfltk-gl1.3t64_1.3.8-6.1_armhf.deb ...
Unpacking libfltk-gl1.3t64:armhf (1.3.8-6.1) ...
Selecting previously unselected package libgl2ps1.4.
Preparing to unpack .../363-libgl2ps1.4_1.4.2+dfsg1-2_armhf.deb ...
Unpacking libgl2ps1.4 (1.4.2+dfsg1-2) ...
Selecting previously unselected package libltdl7:armhf.
Preparing to unpack .../364-libltdl7_2.4.7-7+b1_armhf.deb ...
Unpacking libltdl7:armhf (2.4.7-7+b1) ...
Selecting previously unselected package libglpk40:armhf.
Preparing to unpack .../365-libglpk40_5.0-1+b1_armhf.deb ...
Unpacking libglpk40:armhf (5.0-1+b1) ...
Selecting previously unselected package libopengl0:armhf.
Preparing to unpack .../366-libopengl0_1.7.0-1+b1_armhf.deb ...
Unpacking libopengl0:armhf (1.7.0-1+b1) ...
Selecting previously unselected package libglu1-mesa:armhf.
Preparing to unpack .../367-libglu1-mesa_9.0.2-1.1+b1_armhf.deb ...
Unpacking libglu1-mesa:armhf (9.0.2-1.1+b1) ...
Selecting previously unselected package libgif7:armhf.
Preparing to unpack .../368-libgif7_5.2.2-1_armhf.deb ...
Unpacking libgif7:armhf (5.2.2-1) ...
Selecting previously unselected package libhwy1t64:armhf.
Preparing to unpack .../369-libhwy1t64_1.2.0-2_armhf.deb ...
Unpacking libhwy1t64:armhf (1.2.0-2) ...
Selecting previously unselected package liblcms2-2:armhf.
Preparing to unpack .../370-liblcms2-2_2.16-2_armhf.deb ...
Unpacking liblcms2-2:armhf (2.16-2) ...
Selecting previously unselected package libimath-3-1-29t64:armhf.
Preparing to unpack .../371-libimath-3-1-29t64_3.1.11-2+b1_armhf.deb ...
Unpacking libimath-3-1-29t64:armhf (3.1.11-2+b1) ...
Selecting previously unselected package libopenexr-3-1-30:armhf.
Preparing to unpack .../372-libopenexr-3-1-30_3.1.5-5.1+b2_armhf.deb ...
Unpacking libopenexr-3-1-30:armhf (3.1.5-5.1+b2) ...
Selecting previously unselected package libjxl0.9:armhf.
Preparing to unpack .../373-libjxl0.9_0.9.2-10_armhf.deb ...
Unpacking libjxl0.9:armhf (0.9.2-10) ...
Selecting previously unselected package libwmflite-0.2-7:armhf.
Preparing to unpack .../374-libwmflite-0.2-7_0.2.13-1.1+b2_armhf.deb ...
Unpacking libwmflite-0.2-7:armhf (0.2.13-1.1+b2) ...
Selecting previously unselected package libgraphicsmagick-q16-3t64.
Preparing to unpack .../375-libgraphicsmagick-q16-3t64_1.4+really1.3.45-1_armhf.deb ...
Unpacking libgraphicsmagick-q16-3t64 (1.4+really1.3.45-1) ...
Selecting previously unselected package libgraphicsmagick++-q16-12t64.
Preparing to unpack .../376-libgraphicsmagick++-q16-12t64_1.4+really1.3.45-1_armhf.deb ...
Unpacking libgraphicsmagick++-q16-12t64 (1.4+really1.3.45-1) ...
Selecting previously unselected package libcurl4t64:armhf.
Preparing to unpack .../377-libcurl4t64_8.10.1-2_armhf.deb ...
Unpacking libcurl4t64:armhf (8.10.1-2) ...
Selecting previously unselected package libaec0:armhf.
Preparing to unpack .../378-libaec0_1.1.3-1_armhf.deb ...
Unpacking libaec0:armhf (1.1.3-1) ...
Selecting previously unselected package libsz2:armhf.
Preparing to unpack .../379-libsz2_1.1.3-1_armhf.deb ...
Unpacking libsz2:armhf (1.1.3-1) ...
Selecting previously unselected package libhdf5-103-1t64:armhf.
Preparing to unpack .../380-libhdf5-103-1t64_1.10.10+repack-4_armhf.deb ...
Unpacking libhdf5-103-1t64:armhf (1.10.10+repack-4) ...
Selecting previously unselected package libasound2-data.
Preparing to unpack .../381-libasound2-data_1.2.12-1_all.deb ...
Unpacking libasound2-data (1.2.12-1) ...
Selecting previously unselected package libasound2t64:armhf.
Preparing to unpack .../382-libasound2t64_1.2.12-1_armhf.deb ...
Unpacking libasound2t64:armhf (1.2.12-1) ...
Selecting previously unselected package libopus0:armhf.
Preparing to unpack .../383-libopus0_1.5.2-2_armhf.deb ...
Unpacking libopus0:armhf (1.5.2-2) ...
Selecting previously unselected package libsamplerate0:armhf.
Preparing to unpack .../384-libsamplerate0_0.2.2-4+b1_armhf.deb ...
Unpacking libsamplerate0:armhf (0.2.2-4+b1) ...
Selecting previously unselected package libjack-jackd2-0:armhf.
Preparing to unpack .../385-libjack-jackd2-0_1.9.22~dfsg-3_armhf.deb ...
Unpacking libjack-jackd2-0:armhf (1.9.22~dfsg-3) ...
Selecting previously unselected package libportaudio2:armhf.
Preparing to unpack .../386-libportaudio2_19.6.0-1.2+b2_armhf.deb ...
Unpacking libportaudio2:armhf (19.6.0-1.2+b2) ...
Selecting previously unselected package libqhull-r8.0:armhf.
Preparing to unpack .../387-libqhull-r8.0_2020.2-6+b1_armhf.deb ...
Unpacking libqhull-r8.0:armhf (2020.2-6+b1) ...
Selecting previously unselected package libqrupdate1:armhf.
Preparing to unpack .../388-libqrupdate1_1.1.5-1_armhf.deb ...
Unpacking libqrupdate1:armhf (1.1.5-1) ...
Selecting previously unselected package libqscintilla2-qt6-l10n.
Preparing to unpack .../389-libqscintilla2-qt6-l10n_2.14.1+dfsg-1_all.deb ...
Unpacking libqscintilla2-qt6-l10n (2.14.1+dfsg-1) ...
Selecting previously unselected package libb2-1:armhf.
Preparing to unpack .../390-libb2-1_0.98.1-1.1+b1_armhf.deb ...
Unpacking libb2-1:armhf (0.98.1-1.1+b1) ...
Selecting previously unselected package libdouble-conversion3:armhf.
Preparing to unpack .../391-libdouble-conversion3_3.3.0-1+b1_armhf.deb ...
Unpacking libdouble-conversion3:armhf (3.3.0-1+b1) ...
Selecting previously unselected package libpcre2-16-0:armhf.
Preparing to unpack .../392-libpcre2-16-0_10.42-4+b1_armhf.deb ...
Unpacking libpcre2-16-0:armhf (10.42-4+b1) ...
Selecting previously unselected package libqt6core6t64:armhf.
Preparing to unpack .../393-libqt6core6t64_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6core6t64:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libwayland-client0:armhf.
Preparing to unpack .../394-libwayland-client0_1.23.0-1_armhf.deb ...
Unpacking libwayland-client0:armhf (1.23.0-1) ...
Selecting previously unselected package libegl-mesa0:armhf.
Preparing to unpack .../395-libegl-mesa0_24.2.4-1_armhf.deb ...
Unpacking libegl-mesa0:armhf (24.2.4-1) ...
Selecting previously unselected package libegl1:armhf.
Preparing to unpack .../396-libegl1_1.7.0-1+b1_armhf.deb ...
Unpacking libegl1:armhf (1.7.0-1+b1) ...
Selecting previously unselected package x11-common.
Preparing to unpack .../397-x11-common_1%3a7.7+23.1_all.deb ...
Unpacking x11-common (1:7.7+23.1) ...
Selecting previously unselected package libice6:armhf.
Preparing to unpack .../398-libice6_2%3a1.0.10-1+b1_armhf.deb ...
Unpacking libice6:armhf (2:1.0.10-1+b1) ...
Selecting previously unselected package libevdev2:armhf.
Preparing to unpack .../399-libevdev2_1.13.3+dfsg-1_armhf.deb ...
Unpacking libevdev2:armhf (1.13.3+dfsg-1) ...
Selecting previously unselected package libmtdev1t64:armhf.
Preparing to unpack .../400-libmtdev1t64_1.1.6-1.2_armhf.deb ...
Unpacking libmtdev1t64:armhf (1.1.6-1.2) ...
Selecting previously unselected package libgudev-1.0-0:armhf.
Preparing to unpack .../401-libgudev-1.0-0_238-5_armhf.deb ...
Unpacking libgudev-1.0-0:armhf (238-5) ...
Selecting previously unselected package libwacom-common.
Preparing to unpack .../402-libwacom-common_2.13.0-1_all.deb ...
Unpacking libwacom-common (2.13.0-1) ...
Selecting previously unselected package libwacom9:armhf.
Preparing to unpack .../403-libwacom9_2.13.0-1_armhf.deb ...
Unpacking libwacom9:armhf (2.13.0-1) ...
Selecting previously unselected package libinput-bin.
Preparing to unpack .../404-libinput-bin_1.26.2-1_armhf.deb ...
Unpacking libinput-bin (1.26.2-1) ...
Selecting previously unselected package libinput10:armhf.
Preparing to unpack .../405-libinput10_1.26.2-1_armhf.deb ...
Unpacking libinput10:armhf (1.26.2-1) ...
Selecting previously unselected package libmd4c0:armhf.
Preparing to unpack .../406-libmd4c0_0.5.2-2+b1_armhf.deb ...
Unpacking libmd4c0:armhf (0.5.2-2+b1) ...
Selecting previously unselected package libdbus-1-3:armhf.
Preparing to unpack .../407-libdbus-1-3_1.14.10-4+b1_armhf.deb ...
Unpacking libdbus-1-3:armhf (1.14.10-4+b1) ...
Selecting previously unselected package libqt6dbus6:armhf.
Preparing to unpack .../408-libqt6dbus6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6dbus6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libsm6:armhf.
Preparing to unpack .../409-libsm6_2%3a1.2.3-1+b1_armhf.deb ...
Unpacking libsm6:armhf (2:1.2.3-1+b1) ...
Selecting previously unselected package libts0t64:armhf.
Preparing to unpack .../410-libts0t64_1.22-1.1_armhf.deb ...
Unpacking libts0t64:armhf (1.22-1.1) ...
Selecting previously unselected package libxcb-util1:armhf.
Preparing to unpack .../411-libxcb-util1_0.4.0-1+b2_armhf.deb ...
Unpacking libxcb-util1:armhf (0.4.0-1+b2) ...
Selecting previously unselected package libxcb-image0:armhf.
Preparing to unpack .../412-libxcb-image0_0.4.0-2+b1_armhf.deb ...
Unpacking libxcb-image0:armhf (0.4.0-2+b1) ...
Selecting previously unselected package libxcb-render-util0:armhf.
Preparing to unpack .../413-libxcb-render-util0_0.3.9-1+b2_armhf.deb ...
Unpacking libxcb-render-util0:armhf (0.3.9-1+b2) ...
Selecting previously unselected package libxcb-cursor0:armhf.
Preparing to unpack .../414-libxcb-cursor0_0.1.4-1+b1_armhf.deb ...
Unpacking libxcb-cursor0:armhf (0.1.4-1+b1) ...
Selecting previously unselected package libxcb-icccm4:armhf.
Preparing to unpack .../415-libxcb-icccm4_0.4.1-1.2_armhf.deb ...
Unpacking libxcb-icccm4:armhf (0.4.1-1.2) ...
Selecting previously unselected package libxcb-keysyms1:armhf.
Preparing to unpack .../416-libxcb-keysyms1_0.4.0-1+b3_armhf.deb ...
Unpacking libxcb-keysyms1:armhf (0.4.0-1+b3) ...
Selecting previously unselected package libxcb-shape0:armhf.
Preparing to unpack .../417-libxcb-shape0_1.17.0-2_armhf.deb ...
Unpacking libxcb-shape0:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-xinput0:armhf.
Preparing to unpack .../418-libxcb-xinput0_1.17.0-2_armhf.deb ...
Unpacking libxcb-xinput0:armhf (1.17.0-2) ...
Selecting previously unselected package libxcb-xkb1:armhf.
Preparing to unpack .../419-libxcb-xkb1_1.17.0-2_armhf.deb ...
Unpacking libxcb-xkb1:armhf (1.17.0-2) ...
Selecting previously unselected package xkb-data.
Preparing to unpack .../420-xkb-data_2.42-1_all.deb ...
Unpacking xkb-data (2.42-1) ...
Selecting previously unselected package libxkbcommon0:armhf.
Preparing to unpack .../421-libxkbcommon0_1.6.0-1+b1_armhf.deb ...
Unpacking libxkbcommon0:armhf (1.6.0-1+b1) ...
Selecting previously unselected package libxkbcommon-x11-0:armhf.
Preparing to unpack .../422-libxkbcommon-x11-0_1.6.0-1+b1_armhf.deb ...
Unpacking libxkbcommon-x11-0:armhf (1.6.0-1+b1) ...
Selecting previously unselected package libqt6gui6:armhf.
Preparing to unpack .../423-libqt6gui6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6gui6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libavahi-common-data:armhf.
Preparing to unpack .../424-libavahi-common-data_0.8-13+b2_armhf.deb ...
Unpacking libavahi-common-data:armhf (0.8-13+b2) ...
Selecting previously unselected package libavahi-common3:armhf.
Preparing to unpack .../425-libavahi-common3_0.8-13+b2_armhf.deb ...
Unpacking libavahi-common3:armhf (0.8-13+b2) ...
Selecting previously unselected package libavahi-client3:armhf.
Preparing to unpack .../426-libavahi-client3_0.8-13+b2_armhf.deb ...
Unpacking libavahi-client3:armhf (0.8-13+b2) ...
Selecting previously unselected package libcups2t64:armhf.
Preparing to unpack .../427-libcups2t64_2.4.10-2_armhf.deb ...
Unpacking libcups2t64:armhf (2.4.10-2) ...
Selecting previously unselected package libqt6widgets6:armhf.
Preparing to unpack .../428-libqt6widgets6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6widgets6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libqt6printsupport6:armhf.
Preparing to unpack .../429-libqt6printsupport6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6printsupport6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libqscintilla2-qt6-15:armhf.
Preparing to unpack .../430-libqscintilla2-qt6-15_2.14.1+dfsg-1+b3_armhf.deb ...
Unpacking libqscintilla2-qt6-15:armhf (2.14.1+dfsg-1+b3) ...
Selecting previously unselected package libqt6core5compat6:armhf.
Preparing to unpack .../431-libqt6core5compat6_6.6.2-2_armhf.deb ...
Unpacking libqt6core5compat6:armhf (6.6.2-2) ...
Selecting previously unselected package libqt6sql6:armhf.
Preparing to unpack .../432-libqt6sql6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6sql6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libqt6help6:armhf.
Preparing to unpack .../433-libqt6help6_6.6.2-4_armhf.deb ...
Unpacking libqt6help6:armhf (6.6.2-4) ...
Selecting previously unselected package libduktape207:armhf.
Preparing to unpack .../434-libduktape207_2.7.0-2+b1_armhf.deb ...
Unpacking libduktape207:armhf (2.7.0-2+b1) ...
Selecting previously unselected package libproxy1v5:armhf.
Preparing to unpack .../435-libproxy1v5_0.5.8-1_armhf.deb ...
Unpacking libproxy1v5:armhf (0.5.8-1) ...
Selecting previously unselected package libqt6network6:armhf.
Preparing to unpack .../436-libqt6network6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6network6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libqt6opengl6:armhf.
Preparing to unpack .../437-libqt6opengl6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6opengl6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libqt6openglwidgets6:armhf.
Preparing to unpack .../438-libqt6openglwidgets6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6openglwidgets6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libqt6xml6:armhf.
Preparing to unpack .../439-libqt6xml6_6.6.2+dfsg-12_armhf.deb ...
Unpacking libqt6xml6:armhf (6.6.2+dfsg-12) ...
Selecting previously unselected package libogg0:armhf.
Preparing to unpack .../440-libogg0_1.3.5-3+b1_armhf.deb ...
Unpacking libogg0:armhf (1.3.5-3+b1) ...
Selecting previously unselected package libflac12t64:armhf.
Preparing to unpack .../441-libflac12t64_1.4.3+ds-2.1_armhf.deb ...
Unpacking libflac12t64:armhf (1.4.3+ds-2.1) ...
Selecting previously unselected package libmp3lame0:armhf.
Preparing to unpack .../442-libmp3lame0_3.100-6+b2_armhf.deb ...
Unpacking libmp3lame0:armhf (3.100-6+b2) ...
Selecting previously unselected package libmpg123-0t64:armhf.
Preparing to unpack .../443-libmpg123-0t64_1.32.7-1_armhf.deb ...
Unpacking libmpg123-0t64:armhf (1.32.7-1) ...
Selecting previously unselected package libvorbis0a:armhf.
Preparing to unpack .../444-libvorbis0a_1.3.7-2_armhf.deb ...
Unpacking libvorbis0a:armhf (1.3.7-2) ...
Selecting previously unselected package libvorbisenc2:armhf.
Preparing to unpack .../445-libvorbisenc2_1.3.7-2_armhf.deb ...
Unpacking libvorbisenc2:armhf (1.3.7-2) ...
Selecting previously unselected package libsndfile1:armhf.
Preparing to unpack .../446-libsndfile1_1.2.2-1+b2_armhf.deb ...
Unpacking libsndfile1:armhf (1.2.2-1+b2) ...
Selecting previously unselected package libspqr4:armhf.
Preparing to unpack .../447-libspqr4_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libspqr4:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libsundials-ida6:armhf.
Preparing to unpack .../448-libsundials-ida6_6.4.1+dfsg1-3+b7_armhf.deb ...
Unpacking libsundials-ida6:armhf (6.4.1+dfsg1-3+b7) ...
Selecting previously unselected package libbtf2:armhf.
Preparing to unpack .../449-libbtf2_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libbtf2:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libklu2:armhf.
Preparing to unpack .../450-libklu2_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libklu2:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package libfftw3-mpi3:armhf.
Preparing to unpack .../451-libfftw3-mpi3_3.3.10-1+b3_armhf.deb ...
Unpacking libfftw3-mpi3:armhf (3.3.10-1+b3) ...
Selecting previously unselected package libhwloc15:armhf.
Preparing to unpack .../452-libhwloc15_2.11.2-1_armhf.deb ...
Unpacking libhwloc15:armhf (2.11.2-1) ...
Selecting previously unselected package libmpich12:armhf.
Preparing to unpack .../453-libmpich12_4.2.0-14_armhf.deb ...
Unpacking libmpich12:armhf (4.2.0-14) ...
Selecting previously unselected package libhdf5-mpich-103-1t64:armhf.
Preparing to unpack .../454-libhdf5-mpich-103-1t64_1.10.10+repack-4_armhf.deb ...
Unpacking libhdf5-mpich-103-1t64:armhf (1.10.10+repack-4) ...
Selecting previously unselected package libcombblas2.0.0t64:armhf.
Preparing to unpack .../455-libcombblas2.0.0t64_2.0.0-3.1+b2_armhf.deb ...
Unpacking libcombblas2.0.0t64:armhf (2.0.0-3.1+b2) ...
Selecting previously unselected package libmetis5:armhf.
Preparing to unpack .../456-libmetis5_5.1.0.dfsg-7+b1_armhf.deb ...
Unpacking libmetis5:armhf (5.1.0.dfsg-7+b1) ...
Selecting previously unselected package libptscotch-7.0:armhf.
Preparing to unpack .../457-libptscotch-7.0_7.0.5-1_armhf.deb ...
Unpacking libptscotch-7.0:armhf (7.0.5-1) ...
Selecting previously unselected package libsuperlu-dist8:armhf.
Preparing to unpack .../458-libsuperlu-dist8_8.2.1+dfsg1-2_armhf.deb ...
Unpacking libsuperlu-dist8:armhf (8.2.1+dfsg1-2) ...
Selecting previously unselected package libhypre-2.29.0:armhf.
Preparing to unpack .../459-libhypre-2.29.0_2.29.0-2+b1_armhf.deb ...
Unpacking libhypre-2.29.0:armhf (2.29.0-2+b1) ...
Selecting previously unselected package hwloc-nox.
Preparing to unpack .../460-hwloc-nox_2.11.2-1_armhf.deb ...
Unpacking hwloc-nox (2.11.2-1) ...
Selecting previously unselected package libslurm41t64.
Preparing to unpack .../461-libslurm41t64_24.05.2-1_armhf.deb ...
Unpacking libslurm41t64 (24.05.2-1) ...
Selecting previously unselected package mpich.
Preparing to unpack .../462-mpich_4.2.0-14_armhf.deb ...
Unpacking mpich (4.2.0-14) ...
Selecting previously unselected package mpi-default-bin.
Preparing to unpack .../463-mpi-default-bin_1.17_armhf.deb ...
Unpacking mpi-default-bin (1.17) ...
Selecting previously unselected package libscalapack-mpich2.2:armhf.
Preparing to unpack .../464-libscalapack-mpich2.2_2.2.1-5_armhf.deb ...
Unpacking libscalapack-mpich2.2:armhf (2.2.1-5) ...
Selecting previously unselected package libmumps-5.6t64:armhf.
Preparing to unpack .../465-libmumps-5.6t64_5.6.2-2.1+b2_armhf.deb ...
Unpacking libmumps-5.6t64:armhf (5.6.2-2.1+b2) ...
Selecting previously unselected package libsuperlu6:armhf.
Preparing to unpack .../466-libsuperlu6_6.0.1+dfsg1-1+b1_armhf.deb ...
Unpacking libsuperlu6:armhf (6.0.1+dfsg1-1+b1) ...
Selecting previously unselected package libumfpack6:armhf.
Preparing to unpack .../467-libumfpack6_1%3a7.8.2+dfsg-2_armhf.deb ...
Unpacking libumfpack6:armhf (1:7.8.2+dfsg-2) ...
Selecting previously unselected package ocl-icd-libopencl1:armhf.
Preparing to unpack .../468-ocl-icd-libopencl1_2.3.2-1+b1_armhf.deb ...
Unpacking ocl-icd-libopencl1:armhf (2.3.2-1+b1) ...
Selecting previously unselected package libpetsc-real3.20:armhf.
Preparing to unpack .../469-libpetsc-real3.20_3.20.6+dfsg1-3_armhf.deb ...
Unpacking libpetsc-real3.20:armhf (3.20.6+dfsg1-3) ...
Selecting previously unselected package libsundials-nvecparallel-petsc6:armhf.
Preparing to unpack .../470-libsundials-nvecparallel-petsc6_6.4.1+dfsg1-3+b7_armhf.deb ...
Unpacking libsundials-nvecparallel-petsc6:armhf (6.4.1+dfsg1-3+b7) ...
Selecting previously unselected package libsundials-sunmatrix4:armhf.
Preparing to unpack .../471-libsundials-sunmatrix4_6.4.1+dfsg1-3+b7_armhf.deb ...
Unpacking libsundials-sunmatrix4:armhf (6.4.1+dfsg1-3+b7) ...
Selecting previously unselected package libsundials-sunlinsol3:armhf.
Preparing to unpack .../472-libsundials-sunlinsol3_6.4.1+dfsg1-3+b7_armhf.deb ...
Unpacking libsundials-sunlinsol3:armhf (6.4.1+dfsg1-3+b7) ...
Selecting previously unselected package libtext-unidecode-perl.
Preparing to unpack .../473-libtext-unidecode-perl_1.30-3_all.deb ...
Unpacking libtext-unidecode-perl (1.30-3) ...
Selecting previously unselected package texinfo-lib.
Preparing to unpack .../474-texinfo-lib_7.1.1-1_armhf.deb ...
Unpacking texinfo-lib (7.1.1-1) ...
Selecting previously unselected package tex-common.
Preparing to unpack .../475-tex-common_6.18_all.deb ...
Unpacking tex-common (6.18) ...
Selecting previously unselected package texinfo.
Preparing to unpack .../476-texinfo_7.1.1-1_all.deb ...
Unpacking texinfo (7.1.1-1) ...
Selecting previously unselected package octave-common.
Preparing to unpack .../477-octave-common_9.2.0-3_all.deb ...
Unpacking octave-common (9.2.0-3) ...
Selecting previously unselected package octave.
Preparing to unpack .../478-octave_9.2.0-3_armhf.deb ...
Unpacking octave (9.2.0-3) ...
Selecting previously unselected package libncurses6:armhf.
Preparing to unpack .../479-libncurses6_6.5-2_armhf.deb ...
Unpacking libncurses6:armhf (6.5-2) ...
Selecting previously unselected package libncurses-dev:armhf.
Preparing to unpack .../480-libncurses-dev_6.5-2_armhf.deb ...
Unpacking libncurses-dev:armhf (6.5-2) ...
Selecting previously unselected package libreadline-dev:armhf.
Preparing to unpack .../481-libreadline-dev_8.2-5_armhf.deb ...
Unpacking libreadline-dev:armhf (8.2-5) ...
Selecting previously unselected package libhdf5-fortran-102t64:armhf.
Preparing to unpack .../482-libhdf5-fortran-102t64_1.10.10+repack-4_armhf.deb ...
Unpacking libhdf5-fortran-102t64:armhf (1.10.10+repack-4) ...
Selecting previously unselected package libhdf5-hl-100t64:armhf.
Preparing to unpack .../483-libhdf5-hl-100t64_1.10.10+repack-4_armhf.deb ...
Unpacking libhdf5-hl-100t64:armhf (1.10.10+repack-4) ...
Selecting previously unselected package libhdf5-hl-fortran-100t64:armhf.
Preparing to unpack .../484-libhdf5-hl-fortran-100t64_1.10.10+repack-4_armhf.deb ...
Unpacking libhdf5-hl-fortran-100t64:armhf (1.10.10+repack-4) ...
Selecting previously unselected package libhdf5-cpp-103-1t64:armhf.
Preparing to unpack .../485-libhdf5-cpp-103-1t64_1.10.10+repack-4_armhf.deb ...
Unpacking libhdf5-cpp-103-1t64:armhf (1.10.10+repack-4) ...
Selecting previously unselected package libhdf5-hl-cpp-100t64:armhf.
Preparing to unpack .../486-libhdf5-hl-cpp-100t64_1.10.10+repack-4_armhf.deb ...
Unpacking libhdf5-hl-cpp-100t64:armhf (1.10.10+repack-4) ...
Selecting previously unselected package zlib1g-dev:armhf.
Preparing to unpack .../487-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1_armhf.deb ...
Unpacking zlib1g-dev:armhf (1:1.3.dfsg+really1.3.1-1) ...
Selecting previously unselected package libjpeg62-turbo-dev:armhf.
Preparing to unpack .../488-libjpeg62-turbo-dev_1%3a2.1.5-3_armhf.deb ...
Unpacking libjpeg62-turbo-dev:armhf (1:2.1.5-3) ...
Selecting previously unselected package libjpeg-dev:armhf.
Preparing to unpack .../489-libjpeg-dev_1%3a2.1.5-3_armhf.deb ...
Unpacking libjpeg-dev:armhf (1:2.1.5-3) ...
Selecting previously unselected package libaec-dev:armhf.
Preparing to unpack .../490-libaec-dev_1.1.3-1_armhf.deb ...
Unpacking libaec-dev:armhf (1.1.3-1) ...
Selecting previously unselected package libbrotli-dev:armhf.
Preparing to unpack .../491-libbrotli-dev_1.1.0-2+b4_armhf.deb ...
Unpacking libbrotli-dev:armhf (1.1.0-2+b4) ...
Selecting previously unselected package libidn2-dev:armhf.
Preparing to unpack .../492-libidn2-dev_2.3.7-2_armhf.deb ...
Unpacking libidn2-dev:armhf (2.3.7-2) ...
Selecting previously unselected package libpkgconf3:armhf.
Preparing to unpack .../493-libpkgconf3_1.8.1-3_armhf.deb ...
Unpacking libpkgconf3:armhf (1.8.1-3) ...
Selecting previously unselected package pkgconf-bin.
Preparing to unpack .../494-pkgconf-bin_1.8.1-3_armhf.deb ...
Unpacking pkgconf-bin (1.8.1-3) ...
Selecting previously unselected package pkgconf:armhf.
Preparing to unpack .../495-pkgconf_1.8.1-3_armhf.deb ...
Unpacking pkgconf:armhf (1.8.1-3) ...
Selecting previously unselected package libnghttp2-dev:armhf.
Preparing to unpack .../496-libnghttp2-dev_1.63.0-1_armhf.deb ...
Unpacking libnghttp2-dev:armhf (1.63.0-1) ...
Selecting previously unselected package libpsl-dev:armhf.
Preparing to unpack .../497-libpsl-dev_0.21.2-1.1_armhf.deb ...
Unpacking libpsl-dev:armhf (0.21.2-1.1) ...
Selecting previously unselected package libgmpxx4ldbl:armhf.
Preparing to unpack .../498-libgmpxx4ldbl_2%3a6.3.0+dfsg-2+b1_armhf.deb ...
Unpacking libgmpxx4ldbl:armhf (2:6.3.0+dfsg-2+b1) ...
Selecting previously unselected package libgmp-dev:armhf.
Preparing to unpack .../499-libgmp-dev_2%3a6.3.0+dfsg-2+b1_armhf.deb ...
Unpacking libgmp-dev:armhf (2:6.3.0+dfsg-2+b1) ...
Selecting previously unselected package libevent-2.1-7t64:armhf.
Preparing to unpack .../500-libevent-2.1-7t64_2.1.12-stable-10_armhf.deb ...
Unpacking libevent-2.1-7t64:armhf (2.1.12-stable-10) ...
Selecting previously unselected package libunbound8:armhf.
Preparing to unpack .../501-libunbound8_1.21.1-1_armhf.deb ...
Unpacking libunbound8:armhf (1.21.1-1) ...
Selecting previously unselected package libgnutls-dane0t64:armhf.
Preparing to unpack .../502-libgnutls-dane0t64_3.8.6-2_armhf.deb ...
Unpacking libgnutls-dane0t64:armhf (3.8.6-2) ...
Selecting previously unselected package libgnutls-openssl27t64:armhf.
Preparing to unpack .../503-libgnutls-openssl27t64_3.8.6-2_armhf.deb ...
Unpacking libgnutls-openssl27t64:armhf (3.8.6-2) ...
Selecting previously unselected package libp11-kit-dev:armhf.
Preparing to unpack .../504-libp11-kit-dev_0.25.5-2_armhf.deb ...
Unpacking libp11-kit-dev:armhf (0.25.5-2) ...
Selecting previously unselected package libtasn1-6-dev:armhf.
Preparing to unpack .../505-libtasn1-6-dev_4.19.0-3+b2_armhf.deb ...
Unpacking libtasn1-6-dev:armhf (4.19.0-3+b2) ...
Selecting previously unselected package nettle-dev:armhf.
Preparing to unpack .../506-nettle-dev_3.10-1_armhf.deb ...
Unpacking nettle-dev:armhf (3.10-1) ...
Selecting previously unselected package libgnutls28-dev:armhf.
Preparing to unpack .../507-libgnutls28-dev_3.8.6-2_armhf.deb ...
Unpacking libgnutls28-dev:armhf (3.8.6-2) ...
Selecting previously unselected package librtmp-dev:armhf.
Preparing to unpack .../508-librtmp-dev_2.4+20151223.gitfa8646d.1-2+b4_armhf.deb ...
Unpacking librtmp-dev:armhf (2.4+20151223.gitfa8646d.1-2+b4) ...
Selecting previously unselected package libssl-dev:armhf.
Preparing to unpack .../509-libssl-dev_3.3.2-1_armhf.deb ...
Unpacking libssl-dev:armhf (3.3.2-1) ...
Selecting previously unselected package libssh2-1-dev:armhf.
Preparing to unpack .../510-libssh2-1-dev_1.11.0-7_armhf.deb ...
Unpacking libssh2-1-dev:armhf (1.11.0-7) ...
Selecting previously unselected package libzstd-dev:armhf.
Preparing to unpack .../511-libzstd-dev_1.5.6+dfsg-1_armhf.deb ...
Unpacking libzstd-dev:armhf (1.5.6+dfsg-1) ...
Selecting previously unselected package libcurl4-openssl-dev:armhf.
Preparing to unpack .../512-libcurl4-openssl-dev_8.10.1-2_armhf.deb ...
Unpacking libcurl4-openssl-dev:armhf (8.10.1-2) ...
Selecting previously unselected package hdf5-helpers.
Preparing to unpack .../513-hdf5-helpers_1.10.10+repack-4_armhf.deb ...
Unpacking hdf5-helpers (1.10.10+repack-4) ...
Selecting previously unselected package libhdf5-dev.
Preparing to unpack .../514-libhdf5-dev_1.10.10+repack-4_armhf.deb ...
Unpacking libhdf5-dev (1.10.10+repack-4) ...
Selecting previously unselected package xorg-sgml-doctools.
Preparing to unpack .../515-xorg-sgml-doctools_1%3a1.11-1.1_all.deb ...
Unpacking xorg-sgml-doctools (1:1.11-1.1) ...
Selecting previously unselected package x11proto-dev.
Preparing to unpack .../516-x11proto-dev_2024.1-1_all.deb ...
Unpacking x11proto-dev (2024.1-1) ...
Selecting previously unselected package libxau-dev:armhf.
Preparing to unpack .../517-libxau-dev_1%3a1.0.9-1+b1_armhf.deb ...
Unpacking libxau-dev:armhf (1:1.0.9-1+b1) ...
Selecting previously unselected package libxdmcp-dev:armhf.
Preparing to unpack .../518-libxdmcp-dev_1%3a1.1.2-3+b1_armhf.deb ...
Unpacking libxdmcp-dev:armhf (1:1.1.2-3+b1) ...
Selecting previously unselected package xtrans-dev.
Preparing to unpack .../519-xtrans-dev_1.4.0-1_all.deb ...
Unpacking xtrans-dev (1.4.0-1) ...
Selecting previously unselected package libxcb1-dev:armhf.
Preparing to unpack .../520-libxcb1-dev_1.17.0-2_armhf.deb ...
Unpacking libxcb1-dev:armhf (1.17.0-2) ...
Selecting previously unselected package libx11-dev:armhf.
Preparing to unpack .../521-libx11-dev_2%3a1.8.7-1+b1_armhf.deb ...
Unpacking libx11-dev:armhf (2:1.8.7-1+b1) ...
Selecting previously unselected package libglx-dev:armhf.
Preparing to unpack .../522-libglx-dev_1.7.0-1+b1_armhf.deb ...
Unpacking libglx-dev:armhf (1.7.0-1+b1) ...
Selecting previously unselected package libgl-dev:armhf.
Preparing to unpack .../523-libgl-dev_1.7.0-1+b1_armhf.deb ...
Unpacking libgl-dev:armhf (1.7.0-1+b1) ...
Selecting previously unselected package libblas-dev:armhf.
Preparing to unpack .../524-libblas-dev_3.12.0-3_armhf.deb ...
Unpacking libblas-dev:armhf (3.12.0-3) ...
Selecting previously unselected package liblapack-dev:armhf.
Preparing to unpack .../525-liblapack-dev_3.12.0-3_armhf.deb ...
Unpacking liblapack-dev:armhf (3.12.0-3) ...
Selecting previously unselected package libfftw3-bin.
Preparing to unpack .../526-libfftw3-bin_3.3.10-1+b3_armhf.deb ...
Unpacking libfftw3-bin (3.3.10-1+b3) ...
Selecting previously unselected package libfftw3-dev:armhf.
Preparing to unpack .../527-libfftw3-dev_3.3.10-1+b3_armhf.deb ...
Unpacking libfftw3-dev:armhf (3.3.10-1+b3) ...
Selecting previously unselected package libgfortran-14-dev:armhf.
Preparing to unpack .../528-libgfortran-14-dev_14.2.0-6_armhf.deb ...
Unpacking libgfortran-14-dev:armhf (14.2.0-6) ...
Selecting previously unselected package gfortran-14-arm-linux-gnueabihf.
Preparing to unpack .../529-gfortran-14-arm-linux-gnueabihf_14.2.0-6_armhf.deb ...
Unpacking gfortran-14-arm-linux-gnueabihf (14.2.0-6) ...
Selecting previously unselected package gfortran-14.
Preparing to unpack .../530-gfortran-14_14.2.0-6_armhf.deb ...
Unpacking gfortran-14 (14.2.0-6) ...
Selecting previously unselected package gfortran-arm-linux-gnueabihf.
Preparing to unpack .../531-gfortran-arm-linux-gnueabihf_4%3a14.2.0-1_armhf.deb ...
Unpacking gfortran-arm-linux-gnueabihf (4:14.2.0-1) ...
Selecting previously unselected package gfortran.
Preparing to unpack .../532-gfortran_4%3a14.2.0-1_armhf.deb ...
Unpacking gfortran (4:14.2.0-1) ...
Selecting previously unselected package octave-dev.
Preparing to unpack .../533-octave-dev_9.2.0-3_armhf.deb ...
Unpacking octave-dev (9.2.0-3) ...
Selecting previously unselected package dh-octave.
Preparing to unpack .../534-dh-octave_1.8.0_all.deb ...
Unpacking dh-octave (1.8.0) ...
Selecting previously unselected package octave-io.
Preparing to unpack .../535-octave-io_2.6.4-3+b2_armhf.deb ...
Unpacking octave-io (2.6.4-3+b2) ...
Setting up libapt-pkg-perl (0.1.40+b5) ...
Setting up liblz1:armhf (1.15~pre1-1) ...
Setting up libhwy1t64:armhf (1.2.0-2) ...
Setting up libexpat1:armhf (2.6.3-1) ...
Setting up libmodule-pluggable-perl (5.2-5) ...
Setting up libb2-1:armhf (0.98.1-1.1+b1) ...
Setting up libpipeline1:armhf (1.5.8-1) ...
Setting up libgraphite2-3:armhf (1.3.14-2) ...
Setting up libstring-escape-perl (2010.002-3) ...
Setting up libgnutls-openssl27t64:armhf (3.8.6-2) ...
Setting up liblcms2-2:armhf (2.16-2) ...
Setting up libberkeleydb-perl:armhf (0.64-2+b3) ...
Setting up libpixman-1-0:armhf (0.42.2-1+b1) ...
Setting up plzip (1.11-2) ...
update-alternatives: using /usr/bin/lzip.plzip to provide /usr/bin/lzip (lzip) in auto mode
update-alternatives: using /usr/bin/lzip.plzip to provide /usr/bin/lzip-compressor (lzip-compressor) in auto mode
update-alternatives: using /usr/bin/lzip.plzip to provide /usr/bin/lzip-decompressor (lzip-decompressor) in auto mode
Setting up libsharpyuv0:armhf (1.4.0-0.1) ...
Setting up libwayland-server0:armhf (1.23.0-1) ...
Setting up libaom3:armhf (3.9.1-1) ...
Setting up libfile-which-perl (1.27-2) ...
Setting up libxau6:armhf (1:1.0.9-1+b1) ...
Setting up libxdmcp6:armhf (1:1.1.2-3+b1) ...
Setting up libdouble-conversion3:armhf (3.3.0-1+b1) ...
Setting up libkeyutils1:armhf (1.6.3-3) ...
Setting up libxcb1:armhf (1.17.0-2) ...
Setting up libunicode-utf8-perl (0.62-2+b2) ...
Setting up libfftw3-single3:armhf (3.3.10-1+b3) ...
Setting up libset-intspan-perl (1.19-3) ...
Setting up libicu72:armhf (72.1-5) ...
Setting up libxcb-xfixes0:armhf (1.17.0-2) ...
Setting up libogg0:armhf (1.3.5-3+b1) ...
Setting up libmouse-perl:armhf (2.5.11-1) ...
Setting up libzstd-dev:armhf (1.5.6+dfsg-1) ...
Setting up liblerc4:armhf (4.0.0+ds-4+b1) ...
Setting up libpod-pom-perl (2.01-4) ...
Setting up bsdextrautils (2.40.2-9) ...
Setting up hdf5-helpers (1.10.10+repack-4) ...
Setting up libwmflite-0.2-7:armhf (0.2.13-1.1+b2) ...
Setting up libregexp-pattern-perl (0.2.14-2) ...
Setting up libdata-messagepack-perl (1.02-1+b3) ...
Setting up libclass-inspector-perl (1.36-3) ...
Setting up libxcb-xinput0:armhf (1.17.0-2) ...
Setting up libdynaloader-functions-perl (0.004-1) ...
Setting up libdatrie1:armhf (0.2.13-3) ...
Setting up libtext-glob-perl (0.11-3) ...
Setting up libclass-method-modifiers-perl (2.15-1) ...
Setting up liblist-compare-perl (0.55-2) ...
Setting up libmagic-mgc (1:5.45-3) ...
Setting up libxcb-render0:armhf (1.17.0-2) ...
Setting up libclone-perl:armhf (0.47-1) ...
Setting up libarchive-zip-perl (1.68-1) ...
Setting up libyaml-0-2:armhf (0.2.5-1+b1) ...
Setting up libsub-identify-perl (0.14-3+b2) ...
Setting up libcpanel-json-xs-perl:armhf (4.38-1) ...
Setting up libglvnd0:armhf (1.7.0-1+b1) ...
Setting up libio-stringy-perl (2.113-2) ...
Setting up libhtml-tagset-perl (3.24-1) ...
Setting up libts0t64:armhf (1.22-1.1) ...
Setting up liblog-any-perl (1.717-1) ...
Setting up libyaml-pp-perl (0.38.0-1) ...
Setting up libxcb-glx0:armhf (1.17.0-2) ...
Setting up libdevel-size-perl (0.84-1) ...
Setting up unzip (6.0-28) ...
Setting up libdebhelper-perl (13.20) ...
Setting up libbrotli1:armhf (1.1.0-2+b4) ...
Setting up libedit2:armhf (3.1-20240808-1) ...
Setting up libregexp-pattern-license-perl (3.11.2-1) ...
Setting up libconvert-binhex-perl (1.125-3) ...
Setting up liblwp-mediatypes-perl (6.04-2) ...
Setting up libmagic1t64:armhf (1:5.45-3) ...
Setting up libyaml-libyaml-perl (0.902.0+ds-2) ...
Setting up fonts-freefont-otf (20211204+svn4273-2) ...
Setting up libio-interactive-perl (1.025-1) ...
Setting up libxcb-keysyms1:armhf (0.4.0-1+b3) ...
Setting up libxcb-shape0:armhf (1.17.0-2) ...
Setting up x11-common (1:7.7+23.1) ...
invoke-rc.d: could not determine current runlevel
Setting up X socket directories... /tmp/.X11-unix /tmp/.ICE-unix.
Setting up libtry-tiny-perl (0.32-1) ...
Setting up libsensors-config (1:3.6.0-10) ...
Setting up libpsl5t64:armhf (0.21.2-1.1) ...
Setting up libnghttp2-14:armhf (1.63.0-1) ...
Setting up libdeflate0:armhf (1.22-1) ...
Setting up perl-openssl-defaults:armhf (7+b2) ...
Setting up libmldbm-perl (2.05-4) ...
Setting up libxml-namespacesupport-perl (1.12-2) ...
Setting up gettext-base (0.22.5-2) ...
Setting up m4 (1.4.19-4) ...
Setting up libevent-2.1-7t64:armhf (2.1.12-stable-10) ...
Setting up libclone-choose-perl (0.010-2) ...
Setting up libqhull-r8.0:armhf (2020.2-6+b1) ...
Setting up libxcb-render-util0:armhf (0.3.9-1+b2) ...
Setting up xkb-data (2.42-1) ...
Setting up liblzo2-2:armhf (2.10-3) ...
Setting up libtime-moment-perl (0.44-2+b3) ...
Setting up libencode-locale-perl (1.05-3) ...
Setting up libmetis5:armhf (5.1.0.dfsg-7+b1) ...
Setting up libxcb-shm0:armhf (1.17.0-2) ...
Setting up libxcb-icccm4:armhf (0.4.1-1.2) ...
Setting up libcom-err2:armhf (1.47.1-1) ...
Setting up file (1:5.45-3) ...
Setting up texinfo-lib (7.1.1-1) ...
Setting up libmpg123-0t64:armhf (1.32.7-1) ...
Setting up libconfig-tiny-perl (2.30-1) ...
Setting up libsereal-encoder-perl (5.004+ds-1+b2) ...
Setting up liblist-utilsby-perl (0.12-2) ...
Setting up libyaml-tiny-perl (1.74-1) ...
Setting up libjbig0:armhf (2.1-6.1+b1) ...
Setting up octave-common (9.2.0-3) ...
Setting up libregexp-common-perl (2024080801-1) ...
Setting up libpcre2-16-0:armhf (10.42-4+b1) ...
Setting up libaec0:armhf (1.1.3-1) ...
Setting up libnet-netmask-perl (2.0002-2) ...
Setting up libopengl0:armhf (1.7.0-1+b1) ...
Setting up libsub-install-perl (0.929-1) ...
Setting up libelf1t64:armhf (0.191-2) ...
Setting up libxcb-util1:armhf (0.4.0-1+b2) ...
Setting up libpsl-dev:armhf (0.21.2-1.1) ...
Setting up libindirect-perl (0.39-2+b3) ...
Setting up libxcb-xkb1:armhf (1.17.0-2) ...
Setting up libxcb-image0:armhf (0.4.0-2+b1) ...
Setting up libkrb5support0:armhf (1.21.3-3) ...
Setting up libnumber-compare-perl (0.03-3) ...
Setting up libsasl2-modules-db:armhf (2.1.28+dfsg1-8) ...
Setting up libxcb-present0:armhf (1.17.0-2) ...
Setting up liberror-perl (0.17029-2) ...
Setting up libasound2-data (1.2.12-1) ...
Setting up libbtf2:armhf (1:7.8.2+dfsg-2) ...
Setting up libjson-maybexs-perl (1.004008-1) ...
Setting up libxml-sax-base-perl (1.09-3) ...
Setting up libio-string-perl (1.08-4) ...
Setting up libboolean-perl (0.46-3) ...
Setting up libnetaddr-ip-perl (4.079+dfsg-2+b3) ...
Setting up xtrans-dev (1.4.0-1) ...
Setting up autotools-dev (20220109.1) ...
Setting up libz3-4:armhf (4.8.12-3.1+b2) ...
Setting up libglib2.0-0t64:armhf (2.82.1-1) ...
No schema files found: doing nothing.
Setting up libblas3:armhf (3.12.0-3) ...
update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so.3 to provide /usr/lib/arm-linux-gnueabihf/libblas.so.3 (libblas.so.3-arm-linux-gnueabihf) in auto mode
Setting up libclass-data-inheritable-perl (0.08-3) ...
Setting up libunbound8:armhf (1.21.1-1) ...
Setting up libpkgconf3:armhf (1.8.1-3) ...
Setting up libgmpxx4ldbl:armhf (2:6.3.0+dfsg-2+b1) ...
Setting up libalgorithm-c3-perl (0.11-2) ...
Setting up libasound2t64:armhf (1.2.12-1) ...
Setting up liblog-log4perl-perl (1.57-1) ...
Setting up libtext-reform-perl (1.20-5) ...
Setting up libjpeg62-turbo:armhf (1:2.1.5-3) ...
Setting up libgnutls-dane0t64:armhf (3.8.6-2) ...
Setting up libx11-data (2:1.8.7-1) ...
Setting up libsvtav1enc2:armhf (2.2.1+dfsg-2) ...
Setting up libjpeg62-turbo-dev:armhf (1:2.1.5-3) ...
Setting up libfile-find-rule-perl (0.34-3) ...
Setting up libxcb-sync1:armhf (1.17.0-2) ...
Setting up librtmp1:armhf (2.4+20151223.gitfa8646d.1-2+b4) ...
Setting up libipc-system-simple-perl (1.30-2) ...
Setting up libio-tiecombine-perl (1.005-3) ...
Setting up libnet-domain-tld-perl (1.75-4) ...
Setting up libperlio-utf8-strict-perl (0.010-1+b2) ...
Setting up aglfn (1.7+git20191031.4036a9c-2) ...
Setting up libxcb-cursor0:armhf (0.1.4-1+b1) ...
Setting up libavahi-common-data:armhf (0.8-13+b2) ...
Setting up libncurses6:armhf (6.5-2) ...
Setting up libdbus-1-3:armhf (1.14.10-4+b1) ...
Setting up libfribidi0:armhf (1.0.15-1) ...
Setting up libopus0:armhf (1.5.2-2) ...
Setting up t1utils (1.41-4) ...
Setting up diffstat (1.66-1) ...
Setting up libimagequant0:armhf (2.18.0-1+b1) ...
Setting up fonts-dejavu-mono (2.37-8) ...
Setting up libssl-dev:armhf (3.3.2-1) ...
Setting up libpng16-16t64:armhf (1.6.44-2) ...
Setting up libhwloc15:armhf (2.11.2-1) ...
Setting up libimath-3-1-29t64:armhf (3.1.11-2+b1) ...
Setting up libvorbis0a:armhf (1.3.7-2) ...
Setting up libvariable-magic-perl (0.64-1) ...
Setting up libio-html-perl (1.004-3) ...
Setting up libtext-template-perl (1.61-1) ...
Setting up libpod-parser-perl (1.67-1) ...
Setting up autopoint (0.22.5-2) ...
Setting up libb-hooks-op-check-perl:armhf (0.22-3+b1) ...
Setting up fonts-dejavu-core (2.37-8) ...
Setting up liblist-moreutils-xs-perl (0.430-4) ...
Setting up pkgconf-bin (1.8.1-3) ...
Setting up libsensors5:armhf (1:3.6.0-10) ...
Setting up libk5crypto3:armhf (1.21.3-3) ...
Setting up libqscintilla2-qt6-l10n (2.14.1+dfsg-1) ...
Setting up libltdl7:armhf (2.4.7-7+b1) ...
Setting up libidn2-dev:armhf (2.3.7-2) ...
Setting up libfftw3-double3:armhf (3.3.10-1+b3) ...
Setting up libglapi-mesa:armhf (24.2.4-1) ...
Setting up libparams-util-perl (1.102-3) ...
Setting up libsasl2-2:armhf (2.1.28+dfsg1-8) ...
Setting up libgfortran5:armhf (14.2.0-6) ...
Setting up libvulkan1:armhf (1.3.290.0-1) ...
Setting up libtime-duration-perl (1.21-2) ...
Setting up autoconf (2.72-3) ...
Setting up libtext-xslate-perl:armhf (3.5.9-2) ...
Setting up libnghttp3-9:armhf (1.4.0-1) ...
Setting up libsub-exporter-progressive-perl (0.001013-3) ...
Setting up libwebp7:armhf (1.4.0-0.1) ...
Setting up libarray-intspan-perl (2.004-2) ...
Setting up libcapture-tiny-perl (0.48-2) ...
Setting up libtimedate-perl (2.3300-2) ...
Setting up libexporter-lite-perl (0.09-2) ...
Setting up libxcb-dri2-0:armhf (1.17.0-2) ...
Setting up libsub-name-perl:armhf (0.27-1+b2) ...
Setting up libgif7:armhf (5.2.2-1) ...
Setting up zlib1g-dev:armhf (1:1.3.dfsg+really1.3.1-1) ...
Setting up dwz (0.15-1+b2) ...
Setting up libdata-validate-domain-perl (0.15-1) ...
Setting up libproc-processtable-perl:armhf (0.636-1+b2) ...
Setting up libparse-recdescent-perl (1.967015+dfsg-4) ...
Setting up libdav1d7:armhf (1.4.3-1) ...
Setting up libmpich12:armhf (4.2.0-14) ...
Setting up libmtdev1t64:armhf (1.1.6-1.2) ...
Setting up sensible-utils (0.0.24) ...
Setting up ocl-icd-libopencl1:armhf (2.3.2-1+b1) ...
Setting up libduktape207:armhf (2.7.0-2+b1) ...
Setting up libxshmfence1:armhf (1.3-1+b1) ...
Setting up libtiff6:armhf (4.5.1+git230720-5) ...
Setting up libxcb-randr0:armhf (1.17.0-2) ...
Setting up librav1e0.7:armhf (0.7.1-7+b1) ...
Setting up libpath-tiny-perl (0.146-1) ...
Setting up libuchardet0:armhf (0.0.8-1+b1) ...
Setting up lzop (1.04-2) ...
Setting up libassuan9:armhf (3.0.1-2) ...
Setting up libjson-perl (4.10000-1) ...
Setting up liblog-any-adapter-screen-perl (0.141-1) ...
Setting up librole-tiny-perl (2.002004-1) ...
Setting up libipc-run3-perl (0.049-1) ...
Setting up libmd4c0:armhf (0.5.2-2+b1) ...
Setting up libregexp-wildcards-perl (1.05-3) ...
Setting up libmousex-strictconstructor-perl (0.02-3) ...
Setting up libfile-sharedir-perl (1.118-3) ...
Setting up libsub-uplevel-perl (0.2800-3) ...
Setting up libsuitesparseconfig7:armhf (1:7.8.2+dfsg-2) ...
Setting up liblua5.4-0:armhf (5.4.6-3+b1) ...
Setting up libx11-6:armhf (2:1.8.7-1+b1) ...
Setting up libaliased-perl (0.34-3) ...
Setting up libslurm41t64 (24.05.2-1) ...
Setting up libthai-data (0.1.29-2) ...
Setting up xorg-sgml-doctools (1:1.11-1.1) ...
Setting up netbase (6.4) ...
Setting up libabsl20230802:armhf (20230802.1-4) ...
Setting up libngtcp2-16:armhf (1.6.0-1) ...
Setting up libstrictures-perl (2.000006-1) ...
Setting up libsub-quote-perl (2.006008-1) ...
Setting up libdevel-stacktrace-perl (2.0500-1) ...
Setting up libclass-xsaccessor-perl (1.19-4+b3) ...
Setting up libtext-autoformat-perl (1.750000-2) ...
Setting up libkrb5-3:armhf (1.21.3-3) ...
Setting up libglu1-mesa:armhf (9.0.2-1.1+b1) ...
Setting up libflac12t64:armhf (1.4.3+ds-2.1) ...
Setting up libtoml-tiny-perl (0.18-1) ...
Setting up libstemmer0d:armhf (2.2.0-4+b1) ...
Setting up libsort-versions-perl (1.62-3) ...
Setting up libssh2-1t64:armhf (1.11.0-7) ...
Setting up libexporter-tiny-perl (1.006002-1) ...
Setting up libterm-readkey-perl (2.38-2+b3) ...
Setting up libtext-unidecode-perl (1.30-3) ...
Setting up libde265-0:armhf (1.0.15-1+b2) ...
Setting up libfont-ttf-perl (1.06-2) ...
Setting up libfile-homedir-perl (1.006-2) ...
Setting up libsamplerate0:armhf (0.2.2-4+b1) ...
Setting up libtasn1-6-dev:armhf (4.19.0-3+b2) ...
Setting up openssl (3.3.2-1) ...
Setting up libwebpmux3:armhf (1.4.0-0.1) ...
Setting up libtext-levenshteinxs-perl (0.03-5+b3) ...
Setting up libperlio-gzip-perl (0.20-1+b3) ...
Setting up libdrm-common (2.4.123-1) ...
Setting up libyuv0:armhf (0.0.1888.20240710-3) ...
Setting up libevdev2:armhf (1.13.3+dfsg-1) ...
Setting up readline-common (8.2-5) ...
Setting up libxml2:armhf (2.12.7+dfsg+really2.9.14-0.1) ...
Setting up libhtml-html5-entities-perl (0.004-3) ...
Setting up libtext-levenshtein-damerau-perl (0.41-3) ...
Setting up libsereal-decoder-perl (5.004+ds-1+b2) ...
Setting up libmarkdown2:armhf (2.2.7-2+b1) ...
Setting up liburi-perl (5.30-1) ...
Setting up iso-codes (4.17.0-1) ...
Setting up libnet-ipv6addr-perl (1.02-1) ...
Setting up libbrotli-dev:armhf (1.1.0-2+b4) ...
Setting up libgudev-1.0-0:armhf (238-5) ...
Setting up libngtcp2-crypto-gnutls8:armhf (1.6.0-1) ...
Setting up libp11-kit-dev:armhf (0.25.5-2) ...
Setting up libmp3lame0:armhf (3.100-6+b2) ...
Setting up libblas-dev:armhf (3.12.0-3) ...
update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so to provide /usr/lib/arm-linux-gnueabihf/libblas.so (libblas.so-arm-linux-gnueabihf) in auto mode
Setting up libsz2:armhf (1.1.3-1) ...
Setting up libvorbisenc2:armhf (1.3.7-2) ...
Setting up libdata-validate-ip-perl (0.31-1) ...
Setting up libwacom-common (2.13.0-1) ...
Setting up libmousex-nativetraits-perl (1.09-3) ...
Setting up libemail-address-xs-perl (1.05-1+b3) ...
Setting up libxkbcommon0:armhf (1.6.0-1+b1) ...
Setting up libwayland-client0:armhf (1.23.0-1) ...
Setting up libnet-ssleay-perl:armhf (1.94-1+b1) ...
Setting up automake (1:1.16.5-1.3) ...
update-alternatives: using /usr/bin/automake-1.16 to provide /usr/bin/automake (automake) in auto mode
Setting up libgfortran-14-dev:armhf (14.2.0-6) ...
Setting up x11proto-dev (2024.1-1) ...
Setting up libfile-stripnondeterminism-perl (1.14.0-1) ...
Setting up libxcb-dri3-0:armhf (1.17.0-2) ...
Setting up gnuplot-data (6.0.0+dfsg1-3) ...
Setting up libllvm19:armhf (1:19.1.1-1) ...
Setting up libx11-xcb1:armhf (2:1.8.7-1+b1) ...
Setting up libice6:armhf (2:1.0.10-1+b1) ...
Setting up libhttp-date-perl (6.06-1) ...
Setting up libptscotch-7.0:armhf (7.0.5-1) ...
Setting up liblapack3:armhf (3.12.0-3) ...
update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so.3 to provide /usr/lib/arm-linux-gnueabihf/liblapack.so.3 (liblapack.so.3-arm-linux-gnueabihf) in auto mode
Setting up libncurses-dev:armhf (6.5-2) ...
Setting up libfile-basedir-perl (0.09-2) ...
Setting up gettext (0.22.5-2) ...
Setting up libarpack2t64:armhf (3.9.1-1.1+b1) ...
Setting up libgmp-dev:armhf (2:6.3.0+dfsg-2+b1) ...
Setting up libamd3:armhf (1:7.8.2+dfsg-2) ...
Setting up libfile-listing-perl (6.16-1) ...
Setting up libxau-dev:armhf (1:1.0.9-1+b1) ...
Setting up libxpm4:armhf (1:3.5.17-1+b1) ...
Setting up nettle-dev:armhf (3.10-1) ...
Setting up libxrender1:armhf (1:0.9.10-1.1+b1) ...
Setting up libtool (2.4.7-7) ...
Setting up libcolamd3:armhf (1:7.8.2+dfsg-2) ...
Setting up libfftw3-bin (3.3.10-1+b3) ...
Setting up libwacom9:armhf (2.13.0-1) ...
Setting up fontconfig-config (2.15.0-1.1) ...
Setting up liblist-moreutils-perl (0.430-2) ...
Setting up libxmlb2:armhf (0.3.20-1) ...
Setting up libpod-constants-perl (0.19-2) ...
Setting up libhash-merge-perl (0.302-1) ...
Setting up hwloc-nox (2.11.2-1) ...
Setting up libsoftware-copyright-perl (0.012-2) ...
Setting up libaec-dev:armhf (1.1.3-1) ...
Setting up libavahi-common3:armhf (0.8-13+b2) ...
Setting up libcxsparse4:armhf (1:7.8.2+dfsg-2) ...
Setting up libjpeg-dev:armhf (1:2.1.5-3) ...
Setting up libxext6:armhf (2:1.3.4-1+b2) ...
Setting up libnet-http-perl (6.23-1) ...
Setting up libpath-iterator-rule-perl (1.015-2) ...
Setting up libtext-markdown-discount-perl (0.16-1+b2) ...
Setting up libcombblas2.0.0t64:armhf (2.0.0-3.1+b2) ...
Setting up libfftw3-mpi3:armhf (3.3.10-1+b3) ...
Setting up libexception-class-perl (1.45-1) ...
Setting up libsundials-sunmatrix4:armhf (6.4.1+dfsg1-3+b7) ...
Setting up libclass-c3-perl (0.35-2) ...
Setting up libsuperlu6:armhf (6.0.1+dfsg1-1+b1) ...
Setting up libqrupdate1:armhf (1.1.5-1) ...
Setting up libdevel-callchecker-perl:armhf (0.009-1) ...
Setting up libldap-2.5-0:armhf (2.5.18+dfsg-3) ...
Setting up libsundials-ida6:armhf (6.4.1+dfsg1-3+b7) ...
Setting up libcamd3:armhf (1:7.8.2+dfsg-2) ...
Setting up pkgconf:armhf (1.8.1-3) ...
Setting up libxxf86vm1:armhf (1:1.1.4-1+b3) ...
Setting up libinput-bin (1.26.2-1) ...
Setting up libxs-parse-sublike-perl:armhf (0.28-1) ...
Setting up intltool-debian (0.35.0+20060710.6) ...
Setting up dh-autoreconf (20) ...
Setting up patchutils (0.4.2-1) ...
Setting up libthai0:armhf (0.1.29-2) ...
Setting up ca-certificates (20240203) ...
Updating certificates in /etc/ssl/certs...
146 added, 0 removed; done.
Setting up libxdmcp-dev:armhf (1:1.1.2-3+b1) ...
Setting up libdata-validate-uri-perl (0.07-3) ...
Setting up libxs-parse-keyword-perl (0.46-1) ...
Setting up libtest-exception-perl (0.43-3) ...
Setting up libfreetype6:armhf (2.13.3+dfsg-1) ...
Setting up libglpk40:armhf (5.0-1+b1) ...
Setting up libxfixes3:armhf (1:6.0.0-2+b1) ...
Setting up libstring-copyright-perl (0.003014-1) ...
Setting up libopenexr-3-1-30:armhf (3.1.5-5.1+b2) ...
Setting up shared-mime-info (2.4-5+b1) ...
Setting up libxinerama1:armhf (2:1.1.4-3+b1) ...
Setting up libxkbcommon-x11-0:armhf (1.6.0-1+b1) ...
Setting up liblapack-dev:armhf (3.12.0-3) ...
update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so to provide /usr/lib/arm-linux-gnueabihf/liblapack.so (liblapack.so-arm-linux-gnueabihf) in auto mode
Setting up libgssapi-krb5-2:armhf (1.21.3-3) ...
Setting up libdata-optlist-perl (0.114-1) ...
Setting up libgav1-1:armhf (0.19.0-2+b1) ...
Setting up ucf (3.0043+nmu1) ...
Setting up libssh2-1-dev:armhf (1.11.0-7) ...
Setting up libccolamd3:armhf (1:7.8.2+dfsg-2) ...
Setting up libreadline8t64:armhf (8.2-5) ...
Setting up dh-strip-nondeterminism (1.14.0-1) ...
Setting up libwww-robotrules-perl (6.02-1) ...
Setting up libsyntax-keyword-try-perl (0.30-1) ...
Setting up libjack-jackd2-0:armhf (1.9.22~dfsg-3) ...
Setting up libdrm2:armhf (2.4.123-1) ...
Setting up groff-base (1.23.0-5) ...
Setting up libhtml-parser-perl:armhf (3.83-1) ...
Setting up gpgconf (2.2.44-1) ...
Setting up libharfbuzz0b:armhf (9.0.0-1) ...
Setting up libfontconfig1:armhf (2.15.0-1.1) ...
Setting up libsndfile1:armhf (1.2.2-1+b2) ...
Setting up libmro-compat-perl (0.15-2) ...
Setting up libsm6:armhf (2:1.2.3-1+b1) ...
Setting up libfftw3-dev:armhf (3.3.10-1+b3) ...
Setting up libavahi-client3:armhf (0.8-13+b2) ...
Setting up libio-socket-ssl-perl (2.089-1) ...
Setting up gpg (2.2.44-1) ...
Setting up libsub-exporter-perl (0.990-1) ...
Setting up libhttp-message-perl (7.00-2) ...
Setting up gfortran-14-arm-linux-gnueabihf (14.2.0-6) ...
Setting up libdrm-amdgpu1:armhf (2.4.123-1) ...
Setting up libhtml-form-perl (6.11-2) ...
Setting up libiterator-perl (0.03+ds1-2) ...
Setting up libgnutls28-dev:armhf (3.8.6-2) ...
Setting up libinput10:armhf (1.26.2-1) ...
Setting up libnghttp2-dev:armhf (1.63.0-1) ...
Setting up libportaudio2:armhf (19.6.0-1.2+b2) ...
Setting up libqt6core6t64:armhf (6.6.2+dfsg-12) ...
Setting up libhttp-negotiate-perl (6.01-2) ...
Setting up fontconfig (2.15.0-1.1) ...
Regenerating fonts cache... done.
Setting up libavif16:armhf (1.1.1-1) ...
Setting up libcarp-assert-more-perl (2.4.0-1) ...
Setting up libcholmod5:armhf (1:7.8.2+dfsg-2) ...
Setting up libxft2:armhf (2.3.6-1+b1) ...
Setting up libfeature-compat-try-perl (0.05-1) ...
Setting up libxcb1-dev:armhf (1.17.0-2) ...
Setting up libiterator-util-perl (0.02+ds1-2) ...
Setting up libcurl4t64:armhf (8.10.1-2) ...
Setting up libhttp-cookies-perl (6.11-1) ...
Setting up libspqr4:armhf (1:7.8.2+dfsg-2) ...
Setting up libsuperlu-dist8:armhf (8.2.1+dfsg1-2) ...
Setting up libdrm-radeon1:armhf (2.4.123-1) ...
Setting up mpich (4.2.0-14) ...
update-alternatives: using /usr/bin/mpicc.mpich to provide /usr/bin/mpicc (mpi) in auto mode
update-alternatives: using /usr/bin/mpirun.mpich to provide /usr/bin/mpirun (mpirun) in auto mode
Setting up po-debconf (1.0.21+nmu1) ...
Setting up libhtml-tree-perl (5.07-3) ...
Setting up libxcursor1:armhf (1:1.2.2-1) ...
Setting up libparams-classify-perl:armhf (0.015-2+b3) ...
Setting up libpango-1.0-0:armhf (1.54.0+ds-2) ...
Setting up libcgi-pm-perl (4.66-1) ...
Setting up mpi-default-bin (1.17) ...
Setting up libx11-dev:armhf (2:1.8.7-1+b1) ...
Setting up libcurl3t64-gnutls:armhf (8.10.1-2) ...
Setting up libjxl0.9:armhf (0.9.2-10) ...
Setting up libappstream5:armhf (1.0.3-1) ...
Setting up libscalapack-mpich2.2:armhf (2.2.1-5) ...
Setting up libreadline-dev:armhf (8.2-5) ...
Setting up man-db (2.13.0-1) ...
Not building database; man-db/auto-update is not 'true'.
Setting up libxml-sax-perl (1.02+dfsg-3) ...
update-perl-sax-parsers: Registering Perl SAX parser XML::SAX::PurePerl with priority 10...
update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...

Creating config file /etc/perl/XML/SAX/ParserDetails.ini with new version
Setting up libcairo2:armhf (1.18.2-2) ...
Setting up gfortran-arm-linux-gnueabihf (4:14.2.0-1) ...
Setting up libobject-pad-perl (0.814-1) ...
Setting up tex-common (6.18) ...
update-language: texlive-base not installed and configured, doing nothing!
Setting up appstream (1.0.3-1) ...
? Metadata cache was updated successfully.
Setting up libnet-smtp-ssl-perl (1.04-2) ...
Setting up libhdf5-mpich-103-1t64:armhf (1.10.10+repack-4) ...
Setting up libqt6xml6:armhf (6.6.2+dfsg-12) ...
Setting up libqt6sql6:armhf (6.6.2+dfsg-12) ...
Setting up libmodule-runtime-perl (0.016-2) ...
Setting up libmailtools-perl (2.21-3) ...
Setting up libklu2:armhf (1:7.8.2+dfsg-2) ...
Setting up libmumps-5.6t64:armhf (5.6.2-2.1+b2) ...
Setting up libraqm0:armhf (0.10.1-1+b1) ...
Setting up libconfig-model-perl (2.154-1) ...
Setting up librtmp-dev:armhf (2.4+20151223.gitfa8646d.1-2+b4) ...
Setting up libxml-libxml-perl (2.0207+dfsg+really+2.0134-5) ...
update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX::Parser with priority 50...
update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX with priority 50...
update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
Replacing config file /etc/perl/XML/SAX/ParserDetails.ini with new version
Setting up libumfpack6:armhf (1:7.8.2+dfsg-2) ...
Setting up libconst-fast-perl (0.014-2) ...
Setting up libdata-section-perl (0.200008-1) ...
Setting up libqt6core5compat6:armhf (6.6.2-2) ...
Setting up libpangoft2-1.0-0:armhf (1.54.0+ds-2) ...
Setting up libhdf5-103-1t64:armhf (1.10.10+repack-4) ...
Setting up libdata-dpath-perl (0.60-1) ...
Setting up gfortran-14 (14.2.0-6) ...
Setting up libhypre-2.29.0:armhf (2.29.0-2+b1) ...
Setting up libfltk1.3t64:armhf (1.3.8-6.1) ...
Setting up libcups2t64:armhf (2.4.10-2) ...
Setting up libstring-rewriteprefix-perl (0.009-1) ...
Setting up libpangocairo-1.0-0:armhf (1.54.0+ds-2) ...
Setting up libqt6dbus6:armhf (6.6.2+dfsg-12) ...
Setting up libconfig-model-backend-yaml-perl (2.134-2) ...
Setting up libhdf5-hl-100t64:armhf (1.10.10+repack-4) ...
Setting up mesa-libgallium:armhf (24.2.4-1) ...
Setting up libproxy1v5:armhf (0.5.8-1) ...
Setting up libgbm1:armhf (24.2.4-1) ...
Setting up libmodule-implementation-perl (0.09-2) ...
Setting up libpackage-stash-perl (0.40-1) ...
Setting up libimport-into-perl (1.002005-2) ...
Setting up libmoo-perl (2.005005-1) ...
Setting up libhdf5-cpp-103-1t64:armhf (1.10.10+repack-4) ...
Setting up libgl1-mesa-dri:armhf (24.2.4-1) ...
Setting up liblist-someutils-perl (0.59-1) ...
Setting up libhdf5-fortran-102t64:armhf (1.10.10+repack-4) ...
Setting up debhelper (13.20) ...
Setting up libpetsc-real3.20:armhf (3.20.6+dfsg1-3) ...
update-alternatives: using /usr/lib/arm-linux-gnueabihf/libpetsc_real.so.3.20.6 to provide /usr/lib/arm-linux-gnueabihf/libpetsc.so.3.20 (libpetsc.so.3.20) in auto mode
Setting up libmime-tools-perl (5.515-1) ...
Setting up gfortran (4:14.2.0-1) ...
update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode
update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode
Setting up libsoftware-license-perl (0.104006-1) ...
Setting up libclass-load-perl (0.25-2) ...
Setting up libfeature-compat-class-perl (0.07-1) ...
Setting up libegl-mesa0:armhf (24.2.4-1) ...
Setting up libcurl4-openssl-dev:armhf (8.10.1-2) ...
Setting up texinfo (7.1.1-1) ...
Setting up libsundials-nvecparallel-petsc6:armhf (6.4.1+dfsg1-3+b7) ...
Setting up libqt6network6:armhf (6.6.2+dfsg-12) ...
Setting up libegl1:armhf (1.7.0-1+b1) ...
Setting up libmoox-aliases-perl (0.001006-2) ...
Setting up libhdf5-hl-cpp-100t64:armhf (1.10.10+repack-4) ...
Setting up libparams-validate-perl:armhf (1.31-2+b2) ...
Setting up libb-hooks-endofscope-perl (0.28-1) ...
Setting up libglx-mesa0:armhf (24.2.4-1) ...
Setting up libglx0:armhf (1.7.0-1+b1) ...
Setting up libsoftware-licensemoreutils-perl (1.009-1) ...
Setting up libhdf5-hl-fortran-100t64:armhf (1.10.10+repack-4) ...
Setting up libsundials-sunlinsol3:armhf (6.4.1+dfsg1-3+b7) ...
Setting up libgl1:armhf (1.7.0-1+b1) ...
Setting up libqt6gui6:armhf (6.6.2+dfsg-12) ...
Setting up libhdf5-dev (1.10.10+repack-4) ...
update-alternatives: using /usr/lib/arm-linux-gnueabihf/pkgconfig/hdf5-serial.pc to provide /usr/lib/arm-linux-gnueabihf/pkgconfig/hdf5.pc (hdf5.pc) in auto mode
Setting up libnamespace-clean-perl (0.27-2) ...
Setting up libstring-license-perl (0.0.11-1) ...
Setting up libgetopt-long-descriptive-perl (0.111-1) ...
Setting up libglx-dev:armhf (1.7.0-1+b1) ...
Setting up libgl-dev:armhf (1.7.0-1+b1) ...
Setting up licensecheck (3.3.9-1) ...
Setting up libapp-cmd-perl (0.336-1) ...
Setting up libqt6opengl6:armhf (6.6.2+dfsg-12) ...
Setting up libgl2ps1.4 (1.4.2+dfsg1-2) ...
Setting up libqt6widgets6:armhf (6.6.2+dfsg-12) ...
Setting up libfltk-gl1.3t64:armhf (1.3.8-6.1) ...
Setting up cme (1.040-1) ...
Setting up libqt6openglwidgets6:armhf (6.6.2+dfsg-12) ...
Setting up libqt6printsupport6:armhf (6.6.2+dfsg-12) ...
Setting up libqt6help6:armhf (6.6.2-4) ...
Setting up libqscintilla2-qt6-15:armhf (2.14.1+dfsg-1+b3) ...
Setting up libheif-plugin-dav1d:armhf (1.18.2-2) ...
Setting up liblwp-protocol-https-perl (6.14-1) ...
Setting up libheif-plugin-libde265:armhf (1.18.2-2) ...
Setting up libwww-perl (6.77-1) ...
Setting up libheif1:armhf (1.18.2-2) ...
Setting up libparse-debcontrol-perl (2.005-6) ...
Setting up libhtml-tokeparser-simple-perl (3.16-4) ...
Setting up libwww-mechanize-perl (2.19-1) ...
Setting up libgd3:armhf (2.3.3-12) ...
Setting up gnuplot-nox (6.0.0+dfsg1-3) ...
update-alternatives: using /usr/bin/gnuplot-nox to provide /usr/bin/gnuplot (gnuplot) in auto mode
Setting up libgraphicsmagick-q16-3t64 (1.4+really1.3.45-1) ...
Setting up lintian (2.119.0) ...
Setting up libgraphicsmagick++-q16-12t64 (1.4+really1.3.45-1) ...
Setting up libconfig-model-dpkg-perl (3.005) ...
Setting up dh-octave-autopkgtest (1.8.0) ...
Setting up octave (9.2.0-3) ...
Setting up octave-dev (9.2.0-3) ...
Setting up octave-io (2.6.4-3+b2) ...
Setting up dh-octave (1.8.0) ...
Processing triggers for libc-bin (2.40-3) ...
Processing triggers for ca-certificates (20240203) ...
Updating certificates in /etc/ssl/certs...
0 added, 0 removed; done.
Running hooks in /etc/ca-certificates/update.d...
done.
Reading package lists...
Building dependency tree...
Reading state information...
Reading extended state information...
Initializing package states...
Writing extended state information...
Building tag database...
 -> Finished parsing the build-deps
I: Building the package
I: Running cd /build/reproducible-path/octave-statistics-1.7.0/ && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-buildpackage -us -uc -b && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-genchanges -S  > ../octave-statistics_1.7.0-2_source.changes
dpkg-buildpackage: info: source package octave-statistics
dpkg-buildpackage: info: source version 1.7.0-2
dpkg-buildpackage: info: source distribution unstable
dpkg-buildpackage: info: source changed by Sébastien Villemot <sebastien@debian.org>
 dpkg-source --before-build .
dpkg-buildpackage: info: host architecture armhf
 debian/rules clean
dh clean --buildsystem=octave
   dh_auto_clean -O--buildsystem=octave
	dh_octave_clean
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.7.0'
make[1]: *** No rule to make target 'clean'.
make[1]: *** No rule to make target 'distclean'.
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.7.0'
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.7.0/src'
make[1]: *** No rule to make target 'clean'.
make[1]: *** No rule to make target 'distclean'.
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.7.0/src'
   dh_autoreconf_clean -O--buildsystem=octave
   dh_clean -O--buildsystem=octave
 debian/rules binary
dh binary --buildsystem=octave
   dh_update_autotools_config -O--buildsystem=octave
   dh_autoreconf -O--buildsystem=octave
   dh_octave_version -O--buildsystem=octave
Checking the Octave version... ok
   dh_auto_configure -O--buildsystem=octave
   dh_auto_build -O--buildsystem=octave
   dh_auto_test -O--buildsystem=octave
   create-stamp debian/debhelper-build-stamp
   dh_testroot -O--buildsystem=octave
   dh_prep -O--buildsystem=octave
   dh_auto_install -O--buildsystem=octave
	octave --no-gui --no-history --silent --no-init-file --no-window-system /usr/share/dh-octave/install-pkg.m /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/lib/arm-linux-gnueabihf/octave/packages
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.7.0/src'
/usr/bin/mkoctfile --verbose       editDistance.cc
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    editDistance.cc -o /tmp/oct-qPOo4Y.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security  -o editDistance.oct  /tmp/oct-qPOo4Y.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       libsvmread.cc
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    libsvmread.cc -o /tmp/oct-sPcErJ.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security  -o libsvmread.oct  /tmp/oct-sPcErJ.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       libsvmwrite.cc
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    libsvmwrite.cc -o /tmp/oct-0wT8xI.o
libsvmwrite.cc: In function 'void write(std::string, ColumnVector, SparseMatrix)':
libsvmwrite.cc:75:25: warning: format '%lu' expects argument of type 'long unsigned int', but argument 3 has type 'size_t' {aka 'unsigned int'} [-Wformat=]
   75 |         fprintf(fp ," %lu:%g", (size_t)ir[k]+1, samples[k]);
      |                       ~~^      ~~~~~~~~~~~~~~~
      |                         |                   |
      |                         long unsigned int   size_t {aka unsigned int}
      |                       %u
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security  -o libsvmwrite.oct  /tmp/oct-0wT8xI.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       svmpredict.cc svm.cpp svm_model_octave.cc
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    svmpredict.cc -o /tmp/oct-Cc5PZ1.o
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    svm.cpp -o /tmp/oct-VPsKig.o
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    svm_model_octave.cc -o /tmp/oct-YzZeZq.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security  -o svmpredict.oct  /tmp/oct-Cc5PZ1.o /tmp/oct-VPsKig.o /tmp/oct-YzZeZq.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       svmtrain.cc svm.cpp svm_model_octave.cc
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    svmtrain.cc -o /tmp/oct-CD1eJz.o
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    svm.cpp -o /tmp/oct-cLwQKM.o
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    svm_model_octave.cc -o /tmp/oct-3TOKBB.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security  -o svmtrain.oct  /tmp/oct-CD1eJz.o /tmp/oct-cLwQKM.o /tmp/oct-3TOKBB.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       fcnntrain.cc
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    fcnntrain.cc -o /tmp/oct-so8Llt.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security  -o fcnntrain.oct  /tmp/oct-so8Llt.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       fcnnpredict.cc
g++ -c -D_LARGEFILE_SOURCE -D_FILE_OFFSET_BITS=64 -D_TIME_BITS=64 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security    fcnnpredict.cc -o /tmp/oct-NwOu9p.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -fstack-clash-protection -Wformat -Werror=format-security  -o fcnnpredict.oct  /tmp/oct-NwOu9p.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.7.0/src'
copyfile /build/reproducible-path/octave-statistics-1.7.0/./src/editDistance.oct /build/reproducible-path/octave-statistics-1.7.0/./src/fcnnpredict.oct /build/reproducible-path/octave-statistics-1.7.0/./src/fcnntrain.oct /build/reproducible-path/octave-statistics-1.7.0/./src/libsvmread.oct /build/reproducible-path/octave-statistics-1.7.0/./src/libsvmwrite.oct /build/reproducible-path/octave-statistics-1.7.0/./src/svmpredict.oct /build/reproducible-path/octave-statistics-1.7.0/./src/svmtrain.oct /build/reproducible-path/octave-statistics-1.7.0/./src/editDistance.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/fcnnpredict.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/fcnntrain.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/libsvmread.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/libsvmwrite.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/svmpredict.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/svmtrain.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./inst/arm-unknown-linux-gnueabihf-api-v59
warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/std.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/median.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/var.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/mean.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/mad.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

For information about changes from previous versions of the statistics package, run 'news statistics'.
rm: cannot remove '/build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/doc': Is a directory
   dh_octave_check -O--buildsystem=octave
Checking package...
Run the unit tests...
Checking m files ...
warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/std.m shadows a core library function
warning: called from
    /tmp/tmp.Hr9CRkGynF at line 12 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/median.m shadows a core library function
warning: called from
    /tmp/tmp.Hr9CRkGynF at line 12 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/var.m shadows a core library function
warning: called from
    /tmp/tmp.Hr9CRkGynF at line 12 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/mean.m shadows a core library function
warning: called from
    /tmp/tmp.Hr9CRkGynF at line 12 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/mad.m shadows a core library function
warning: called from
    /tmp/tmp.Hr9CRkGynF at line 12 column 1

[inst/ztest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ztest.m
***** error<ztest: too few input arguments.> ztest ();
***** error<ztest: invalid value for standard deviation.> ...
 ztest ([1, 2, 3, 4], 2, -0.5);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 1.2);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", "val");
***** error<ztest: invalid VALUE for tail.>  ...
 ztest ([1, 2, 3, 4], 1, 2, "tail", "val");
***** error<ztest: invalid VALUE for tail.>  ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "val");
***** error<ztest: invalid VALUE for operating dimension.> ...
 ztest ([1, 2, 3, 4], 1, 2, "dim", 3);
***** error<ztest: invalid VALUE for operating dimension.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "dim", 3);
***** error<ztest: invalid NAME for optional arguments.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "badoption", 3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, mean (MPG, "omitnan"), std (MPG, "omitnan"));
 assert (h, 0);
 assert (pval, 1, 1e-14);
 assert (ci, [22.094; 25.343], 1e-3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, 26, 8);
 assert (h, 1);
 assert (pval, 0.00568359158544743, 1e-14);
 assert (ci, [22.101; 25.335], 1e-3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, 26, 4);
 assert (h, 1);
 assert (pval, 3.184168011941316e-08, 1e-14);
 assert (ci, [22.909; 24.527], 1e-3);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/procrustes.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/procrustes.m
***** demo
 ## Create some random points in two dimensions
 n = 10;
 randn ("seed", 1);
 X = normrnd (0, 1, [n, 2]);

 ## Those same points, rotated, scaled, translated, plus some noise
 S = [0.5, -sqrt(3)/2; sqrt(3)/2, 0.5]; # rotate 60 degrees
 Y = normrnd (0.5*X*S + 2, 0.05, n, 2);

 ## Conform Y to X, plot original X and Y, and transformed Y
 [d, Z] = procrustes (X, Y);
 plot (X(:,1), X(:,2), "rx", Y(:,1), Y(:,2), "b.", Z(:,1), Z(:,2), "bx");
***** demo
 ## Find Procrustes distance and plot superimposed shape

 X = [40 88; 51 88; 35 78; 36 75; 39 72; 44 71; 48 71; 52 74; 55 77];
 Y = [36 43; 48 42; 31 26; 33 28; 37 30; 40 31; 45 30; 48 28; 51 24];
 plot (X(:,1),X(:,2),"x");
 hold on
 plot (Y(:,1),Y(:,2),"o");
 xlim ([0 100]);
 ylim ([0 100]);
 legend ("Target shape (X)", "Source shape (Y)");
 [d, Z] = procrustes (X, Y)
 plot (Z(:,1), Z(:,2), "s");
 legend ("Target shape (X)", "Source shape (Y)", "Transformed shape (Z)");
 hold off
***** demo
 ## Apply Procrustes transformation to larger set of points

 ## Create matrices with landmark points for two triangles
 X = [5, 0; 5, 5; 8, 5];   # target
 Y = [0, 0; 1, 0; 1, 1];   # source

 ## Create a matrix with more points on the source triangle
 Y_mp = [linspace(Y(1,1),Y(2,1),10)', linspace(Y(1,2),Y(2,2),10)'; ...
         linspace(Y(2,1),Y(3,1),10)', linspace(Y(2,2),Y(3,2),10)'; ...
         linspace(Y(3,1),Y(1,1),10)', linspace(Y(3,2),Y(1,2),10)'];

 ## Plot both shapes, including the larger set of points for the source shape
 plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
 hold on
 plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
 plot (Y_mp(:,1), Y_mp(:,2), "ro");
 xlim ([-1 10]);
 ylim ([-1 6]);
 legend ("Target shape (X)", "Source shape (Y)", ...
         "More points on Y", "Location", "northwest");
 hold off

 ## Obtain the Procrustes transformation
 [d, Z, transform] = procrustes (X, Y)

 ## Use the Procrustes transformation to superimpose the more points (Y_mp)
 ## on the source shape onto the target shape, and then visualize the results.
 Z_mp = transform.b * Y_mp * transform.T + transform.c(1,:);
 figure
 plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
 hold on
 plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
 plot (Y_mp(:,1), Y_mp(:,2), "ro");
 xlim ([-1 10]);
 ylim ([-1 6]);
 plot ([Z(:,1); Z(1,1)],[Z(:,2); Z(1,2)],"ks-","MarkerFaceColor","k");
 plot (Z_mp(:,1),Z_mp(:,2),"ks");
 legend ("Target shape (X)", "Source shape (Y)", ...
         "More points on Y", "Transformed source shape (Z)", ...
         "Transformed additional points", "Location", "northwest");
 hold off
***** demo
 ## Compare shapes without reflection

 T = [33, 93; 33, 87; 33, 80; 31, 72; 32, 65; 32, 58; 30, 72; ...
      28, 72; 25, 69; 22, 64; 23, 59; 26, 57; 30, 57];
 S = [48, 83; 48, 77; 48, 70; 48, 65; 49, 59; 49, 56; 50, 66; ...
      52, 66; 56, 65; 58, 61; 57, 57; 54, 56; 51, 55];
 plot (T(:,1), T(:,2), "x-");
 hold on
 plot (S(:,1), S(:,2), "o-");
 legend ("Target shape (d)", "Source shape (b)");
 hold off
 d_false = procrustes (T, S, "reflection", false);
 printf ("Procrustes distance without reflection: %f\n", d_false);
 d_true = procrustes (T, S, "reflection", true);
 printf ("Procrustes distance with reflection: %f\n", d_true);
 d_best = procrustes (T, S, "reflection", "best");
 printf ("Procrustes distance with best fit: %f\n", d_true);
***** error procrustes ();
***** error procrustes (1, 2, 3, 4, 5, 6);
***** error<procrustes: X and Y must be 2-dimensional matrices.> ...
 procrustes (ones (2, 2, 2), ones (2, 2, 2));
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, 3+i]);
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, NaN]);
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, Inf]);
***** error<procrustes: X and Y must have equal number of rows.> ...
 procrustes (ones (10 ,3), ones (11, 3));
***** error<procrustes: X must have at least as many columns as Y.> ...
 procrustes (ones (10 ,3), ones (10, 4));
***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection");
***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
 procrustes (ones (10 ,3), ones (10, 3), true);
***** error<procrustes: invalid value for scaling.> ...
 procrustes (ones (10 ,3), ones (10, 3), "scaling", 0);
***** error<procrustes: invalid value for scaling.> ...
 procrustes (ones (10 ,3), ones (10, 3), "scaling", [true true]);
***** error<procrustes: invalid value for reflection.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection", 1);
***** error<procrustes: invalid value for reflection.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection", "some");
***** error<procrustes: invalid name for optional arguments.> ...
 procrustes (ones (10 ,3), ones (10, 3), "param1", "some");
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/runstest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/runstest.m
***** test
 ## NIST beam deflection data
 ## http://www.itl.nist.gov/div898/handbook/eda/section4/eda425.htm
 data = [-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, ...
          194, -220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, ...
         -175, 162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, ...
         -541, -224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, ...
          131, -507, -254, 199, -311, -495, 143, -46, -579, -90, 136, ...
         -472, -338, 202, -287, -477, 169, -124, -568, 17, 48, -568, -135, ...
          162, -430, -422, 172, -74, -577, -13, 92, -534, -243, 194, -355, ...
         -465, 156, -81, -578, -64, 139, -449, -384, 193, -198, -538, 110, ...
          -44, -577, -6, 66, -552, -164, 161, -460, -344, 205, -281, -504, ...
          134, -28, -576, -118, 156, -437, -381, 200, -220, -540, 83, 11, ...
         -568, -160, 172, -414, -408, 188, -125, -572, -32, 139, -492, ...
         -321, 205, -262, -504, 142, -83, -574, 0, 48, -571, -106, 137, ...
         -501, -266, 190, -391, -406, 194, -186, -553, 83, -13, -577, -49, ...
          103, -515, -280, 201, 300, -506, 131, -45, -578, -80, 138, -462, ...
         -361, 201, -211, -554, 32, 74, -533, -235, 187, -372, -442, 182, ...
         -147, -566, 25, 68, -535, -244, 194, -351, -463, 174, -125, -570, ...
           15, 72, -550, -190, 172, -424, -385, 198, -218, -536, 96];
 [h, p, stats] = runstest (data, median (data));
 expected_h = 1;
 expected_p = 0.008562;
 expected_z = 2.6229;
 assert (h, expected_h);
 assert (p, expected_p, 1E-6);
 assert (stats.z, expected_z, 1E-4);
***** shared x
 x = [45, -60, 1.225, 55.4, -9 27];
***** test
 [h, p, stats] = runstest (x);
 assert (h, 0);
 assert (p, 0.6, 1e-14);
 assert (stats.nruns, 5);
 assert (stats.n1, 3);
 assert (stats.n0, 3);
 assert (stats.z, 0.456435464587638, 1e-14);
***** test
 [h, p, stats] = runstest (x, [], "method", "approximate");
 assert (h, 0);
 assert (p, 0.6481, 1e-4);
 assert (stats.z, 0.456435464587638, 1e-14);
***** test
 [h, p, stats] = runstest (x, [], "tail", "left");
 assert (h, 0);
 assert (p, 0.9, 1e-14);
 assert (stats.z, 1.369306393762915, 1e-14);
***** error<runstest: X must be a vector a scalar values.> runstest (ones (2,20))
***** error<runstest: X must be a vector a scalar values.> runstest (["asdasda"])
***** error<runstest: V must be either a scalar number or> ...
 runstest ([2 3 4 3 2 3 4], "updown")
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", 0)
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", [0.02 0.2])
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", 1.2)
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", -0.05)
***** error<runstest: invalid value for method.> ...
 runstest ([2 3 4 3 2 3 4], [], "method", "some")
***** error<runstest: invalid value for tail.> ...
 runstest ([2 3 4 3 2 3 4], [], "tail", "some")
***** error<runstest: invalid optional argument.> ...
 runstest ([2 3 4 3 2 3 4], [], "option", "some")
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/pdist2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pdist2.m
***** shared x, y, xx
 x = [1, 1, 1; 2, 2, 2; 3, 3, 3];
 y = [0, 0, 0; 1, 2, 3; 0, 2, 4; 4, 7, 1];
 xx = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
***** test
 d = sqrt([3, 5, 11, 45; 12, 2, 8, 30; 27, 5, 11, 21]);
 assert (pdist2 (x, y), d);
***** test
 d = [5.1962, 2.2361, 3.3166, 6.7082; ...
      3.4641, 2.2361, 3.3166, 5.4772];
 i = [3, 1, 1, 1; 2, 3, 3, 2];
 [D, I] = pdist2 (x, y, "euclidean", "largest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [1.7321, 1.4142, 2.8284, 4.5826; ...
      3.4641, 2.2361, 3.3166, 5.4772];
 i = [1, 2, 2, 3;2, 1, 1, 2];
 [D, I] = pdist2 (x, y, "euclidean", "smallest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 6.1644, 5.3852; 1.4142, 6.9282, 8.7750; ...
      3.7417, 7.0711, 9.9499; 6.1644, 10.4881, 10.3441];
 i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
 [D, I] = pdist2 (y, yy, "euclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 38, 29; 2, 48, 77; 14, 50, 99; 38, 110, 107];
 i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
 [D, I] = pdist2 (y, yy, "squaredeuclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 3.3256, 2.7249; 0.7610, 3.3453, 4.4799; ...
      1.8514, 3.3869, 5.0703; 2.5525, 5.0709, 5.1297];
 i = [2, 2, 4; 3, 4, 2; 1, 3, 1; 4, 1, 3];
 [D, I] = pdist2 (y, yy, "seuclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [2.1213, 4.2426, 6.3640; 1.2247, 2.4495, 4.4159; ...
      3.2404, 4.8990, 6.8191; 2.7386, 4.2426, 6.1237];
 assert (pdist2 (y, x, "mahalanobis"), d, 1e-4);
***** test
 xx = [1, 3, 4; 3, 5, 4; 8, 7, 6];
 d = [1.3053, 1.8257, 15.0499; 1.3053, 3.3665, 16.5680];
 i = [2, 2, 2; 3, 4, 4];
 [D, I] = pdist2 (y, xx, "mahalanobis", "smallest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [2.5240, 4.1633, 17.3638; 2.0905, 3.9158, 17.0147];
 i = [1, 1, 3; 4, 3, 1];
 [D, I] = pdist2 (y, xx, "mahalanobis", "largest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [3, 3, 5, 9; 6, 2, 4, 8; 9, 3, 5, 7];
 assert (pdist2 (x, y, "cityblock"), d);
***** test
 d = [1, 2, 3, 6; 2, 1, 2, 5; 3, 2, 3, 4];
 assert (pdist2 (x, y, "chebychev"), d);
***** test
 d = repmat ([NaN, 0.0742, 0.2254, 0.1472], [3, 1]);
 assert (pdist2 (x, y, "cosine"), d, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 0, 0.5; 0, 0, 2; 1.5, 1.5, 2; NaN, NaN, NaN];
 i = [2, 2, 4; 3, 3, 2; 4, 4, 3; 1, 1, 1];
 [D, I] = pdist2 (y, yy, "correlation", "smallest", 4);
 assert ({D, I}, {d, i}, eps);
 [D, I] = pdist2 (y, yy, "spearman", "smallest", 4);
 assert ({D, I}, {d, i}, eps);
***** test
 d = [1, 2/3, 1, 1; 1, 2/3, 1, 1; 1, 2/3, 2/3, 2/3];
 i = [1, 1, 1, 2; 2, 2, 3, 3; 3, 3, 2, 1];
 [D, I] = pdist2 (x, y, "hamming", "largest", 4);
 assert ({D, I}, {d, i}, eps);
 [D, I] = pdist2 (x, y, "jaccard", "largest", 4);
 assert ({D, I}, {d, i}, eps);
***** test
 xx = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 yy = [1, 2, 2, 3; 2, 3, 3, 4];
 [D, I] = pdist2 (x, y, "euclidean", "Smallest", 4);
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [d, i] = pdist2 (x, y, eucldist, "Smallest", 4);
 assert ({D, I}, {d, i});
***** warning<pdist2: matrix is close to singular> ...
 pdist2 (xx, xx, "mahalanobis");
***** error<pdist2: too few input arguments.> pdist2 (1)
***** error<pdist2: X and Y must have equal number of columns.> ...
 pdist2 (ones (4, 5), ones (4))
***** error<pdist2: X and Y must be 2 dimensional matrices.> ...
 pdist2 (ones (4, 2, 3), ones (3, 2))
***** error<pdist2: missing value in optional name/value paired arguments.> ...
 pdist2 (ones (3), ones (3), "euclidean", "Largest")
***** error<pdist2: missing value in optional name/value paired arguments.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "Largest")
***** error<pdist2: invalid NAME in optional pairs of arguments.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "large", 4)
***** error<pdist2: you can only use either Smallest or Largest.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "Largest", 4, "smallest", 5)
***** error<pdist2: Smallest or Largest must be specified to compute second output.> ...
 [d, i] = pdist2(ones (3), ones (3), "minkowski", 3)
***** error<pdist2: DistParameter for standardized euclidean must be a vector of> ...
 pdist2 (ones (3), ones (3), "seuclidean", 3)
***** error<pdist2: DistParameter for standardized euclidean must be a nonnegative> ...
 pdist2 (ones (3), ones (3), "seuclidean", [1, -1, 3])
***** error<pdist2: DistParameter for mahalanobis distance must be a covariance> ...
 pdist2 (ones (3), eye (3), "mahalanobis", eye(2))
***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric> ...
 pdist2 (ones (3), eye (3), "mahalanobis", ones(3))
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", 0)
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", -5)
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", [1, 2])
***** error<pdist2: invalid function handle for distance metric.> ...
 pdist2 (ones (3), ones (3), @(v,m) sqrt(repmat(v,rows(m),1)-m,2))
***** error<pdist2: custom distance function produces wrong output size.> ...
 pdist2 (ones (3), ones (3), @(v,m) sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/fitrgam.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitrgam.m
***** demo
 # Train a RegressionGAM Model for synthetic values

 f1 = @(x) cos (3 *x);
 f2 = @(x) x .^ 3;

 # generate x1 and x2 for f1 and f2
 x1 = 2 * rand (50, 1) - 1;
 x2 = 2 * rand (50, 1) - 1;

 # calculate y
 y = f1(x1) + f2(x2);

 # add noise
 y = y + y .* 0.2 .* rand (50,1);
 X = [x1, x2];

 # create an object
 a = fitrgam (X, y, "tol", 1e-3)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4];
 a = fitrgam (x, y);
 assert ({a.X, a.Y}, {x, y})
 assert ({a.BaseModel.Intercept}, {2.5000})
 assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
 assert ({a.NumObservations, a.NumPredictors}, {4, 3})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.Formula}, {[]})
***** test
 x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
 y = [1; 2; 3; 4];
 pnames = {"A", "B", "C", "D"};
 formula = "Y ~ A + B + C + D + A:C";
 intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
 a = fitrgam (x, y, "predictors", pnames, "formula", formula);
 assert ({a.IntMatrix}, {intMat})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
 assert ({a.Formula}, {formula})
***** error<fitrgam: too few arguments.> fitrgam ()
***** error<fitrgam: too few arguments.> fitrgam (ones(10,2))
***** error<fitrgam: Name-Value arguments must be in pairs.>
 fitrgam (ones (4,2), ones (4, 1), "K")
***** error<fitrgam: number of rows in X and Y must be equal.>
 fitrgam (ones (4,2), ones (3, 1))
***** error<fitrgam: number of rows in X and Y must be equal.>
 fitrgam (ones (4,2), ones (3, 1), "K", 2)
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/mnrfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/mnrfit.m
***** error<mnrfit: too few input arguments.> mnrfit (ones (50,1))
***** error<mnrfit: Predictors must be numeric.> ...
 mnrfit ({1 ;2 ;3 ;4 ;5}, ones (5,1))
***** error<mnrfit: Predictors must be a vector or a 2D matrix.> ...
 mnrfit (ones (50, 4, 2), ones (50, 1))
***** error<mnrfit: Response must be a vector or a 2D matrix.> ...
 mnrfit (ones (50, 4), ones (50, 1, 3))
***** error<mnrfit: Y must have the same number of rows as X.> ...
 mnrfit (ones (50, 4), ones (45,1))
***** error<mnrfit: Response labels must be a character array, a cell vector> ...
 mnrfit (ones (5, 4), {1 ;2 ;3 ;4 ;5})
***** error<mnrfit: optional arguments must be in pairs.> ...
 mnrfit (ones (5, 4), ones (5, 1), "model")
***** error<mnrfit: Y must be a column vector when given as cellstr.> ...
 mnrfit (ones (5, 4), {"q","q";"w","w";"q","q";"w","w";"q","q"})
***** error<mnrfit: Y must contain only 1 and 0 when given as a 2D matrix.> ...
 mnrfit (ones (5, 4), [1, 2; 1, 2; 1, 2; 1, 2; 1, 2])
***** error<mnrfit: Y must contain positive integer category numbers.> ...
 mnrfit (ones (5, 4), [1; -1; 1; 2; 1])
***** error<mnrfit: fitting more than 2 nominal responses not supported.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "nominal")
***** error<mnrfit: fitting hierarchical responses not supported.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "hierarchical")
***** error<mnrfit: model type not recognised.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "whatever")
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_wrap/mle.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/mle.m
***** error <mle: X must be a numeric vector of real values.> mle (ones (2))
***** error <mle: X must be a numeric vector of real values.> mle ("text")
***** error <mle: X must be a numeric vector of real values.> mle ([1, 2, 3, i, 5])
***** error <mle: optional arguments must be in NAME-VALUE pairs.> ...
 mle ([1:50], "distribution")
***** error <mle: 'censoring' argument must have the same size as the input data in X.> ...
 mle ([1:50], "censoring", logical ([1,0,1,0]))
***** error <mle: 'frequency' argument must have the same size as the input data in X.> ...
 mle ([1:50], "frequency", [1,0,1,0])
***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
 mle ([1 0 1 0], "frequency", [-1 1 0 0])
***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
 mle ([1 0 1 0], "distribution", "nbin", "frequency", [-1 1 0 0])
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", [0.05, 0.01])
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", 1)
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", -1)
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", i)
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", -1)
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", [20, 50])
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", [20.3])
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", 3i)
***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 mle ([1:50], "options", 4)
***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 mle ([1:50], "options", struct ("x", 3))
***** error <mle: unknown parameter name.> mle ([1:50], "NAME", "value")
***** error <mle: censoring is not supported for the Bernoulli distribution.> ...
 mle ([1 0 1 0], "distribution", "bernoulli", "censoring", [1 1 0 0])
***** error <mle: invalid data for the Bernoulli distribution.> ...
 mle ([1 2 1 0], "distribution", "bernoulli")
***** error <mle: censoring is not supported for the Beta distribution.> ...
 mle ([1 0 1 0], "distribution", "beta", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "bino", "censoring", [1 1 0 0])
***** error <mle: 'Ntrials' parameter is required for the Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "bino")
***** error <mle: censoring is not supported for the Geometric distribution.> ...
 mle ([1 0 1 0], "distribution", "geo", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Generalized Extreme Value distribution.> ...
 mle ([1 0 1 0], "distribution", "gev", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Generalized Pareto distribution.> ...
 mle ([1 0 1 0], "distribution", "gp", "censoring", [1 1 0 0])
***** error <mle: invalid 'theta' location parameter for the Generalized Pareto distribution.> ...
 mle ([1 0 -1 0], "distribution", "gp")
***** error <mle: censoring is not supported for the Half Normal distribution.> ...
 mle ([1 0 1 0], "distribution", "hn", "censoring", [1 1 0 0])
***** error <mle: invalid 'mu' location parameter for the Half Normal distribution.> ...
 mle ([1 0 -1 0], "distribution", "hn")
***** error <mle: censoring is not supported for the Negative Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "nbin", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Poisson distribution.> ...
 mle ([1 0 1 0], "distribution", "poisson", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Discrete Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unid", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
***** error <mle: unrecognized distribution name.> mle ([1:50], "distribution", "value")
***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_wrap/icdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/icdf.m
***** shared p
 p = [0.05:0.05:0.5];
***** assert (icdf ("Beta", p, 5, 2), betainv (p, 5, 2))
***** assert (icdf ("beta", p, 5, 2), betainv (p, 5, 2))
***** assert (icdf ("Binomial", p, 5, 2), binoinv (p, 5, 2))
***** assert (icdf ("bino", p, 5, 2), binoinv (p, 5, 2))
***** assert (icdf ("Birnbaum-Saunders", p, 5, 2), bisainv (p, 5, 2))
***** assert (icdf ("bisa", p, 5, 2), bisainv (p, 5, 2))
***** assert (icdf ("Burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
***** assert (icdf ("burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
***** assert (icdf ("Cauchy", p, 5, 2), cauchyinv (p, 5, 2))
***** assert (icdf ("cauchy", p, 5, 2), cauchyinv (p, 5, 2))
***** assert (icdf ("Chi-squared", p, 5), chi2inv (p, 5))
***** assert (icdf ("chi2", p, 5), chi2inv (p, 5))
***** assert (icdf ("Extreme Value", p, 5, 2), evinv (p, 5, 2))
***** assert (icdf ("ev", p, 5, 2), evinv (p, 5, 2))
***** assert (icdf ("Exponential", p, 5), expinv (p, 5))
***** assert (icdf ("exp", p, 5), expinv (p, 5))
***** assert (icdf ("F-Distribution", p, 5, 2), finv (p, 5, 2))
***** assert (icdf ("f", p, 5, 2), finv (p, 5, 2))
***** assert (icdf ("Gamma", p, 5, 2), gaminv (p, 5, 2))
***** assert (icdf ("gam", p, 5, 2), gaminv (p, 5, 2))
***** assert (icdf ("Geometric", p, 5), geoinv (p, 5))
***** assert (icdf ("geo", p, 5), geoinv (p, 5))
***** assert (icdf ("Generalized Extreme Value", p, 5, 2, 2), gevinv (p, 5, 2, 2))
***** assert (icdf ("gev", p, 5, 2, 2), gevinv (p, 5, 2, 2))
***** assert (icdf ("Generalized Pareto", p, 5, 2, 2), gpinv (p, 5, 2, 2))
***** assert (icdf ("gp", p, 5, 2, 2), gpinv (p, 5, 2, 2))
***** assert (icdf ("Gumbel", p, 5, 2), gumbelinv (p, 5, 2))
***** assert (icdf ("gumbel", p, 5, 2), gumbelinv (p, 5, 2))
***** assert (icdf ("Half-normal", p, 5, 2), hninv (p, 5, 2))
***** assert (icdf ("hn", p, 5, 2), hninv (p, 5, 2))
***** assert (icdf ("Hypergeometric", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
***** assert (icdf ("hyge", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
***** assert (icdf ("Inverse Gaussian", p, 5, 2), invginv (p, 5, 2))
***** assert (icdf ("invg", p, 5, 2), invginv (p, 5, 2))
***** assert (icdf ("Laplace", p, 5, 2), laplaceinv (p, 5, 2))
***** assert (icdf ("laplace", p, 5, 2), laplaceinv (p, 5, 2))
***** assert (icdf ("Logistic", p, 5, 2), logiinv (p, 5, 2))
***** assert (icdf ("logi", p, 5, 2), logiinv (p, 5, 2))
***** assert (icdf ("Log-Logistic", p, 5, 2), loglinv (p, 5, 2))
***** assert (icdf ("logl", p, 5, 2), loglinv (p, 5, 2))
***** assert (icdf ("Lognormal", p, 5, 2), logninv (p, 5, 2))
***** assert (icdf ("logn", p, 5, 2), logninv (p, 5, 2))
***** assert (icdf ("Nakagami", p, 5, 2), nakainv (p, 5, 2))
***** assert (icdf ("naka", p, 5, 2), nakainv (p, 5, 2))
***** assert (icdf ("Negative Binomial", p, 5, 2), nbininv (p, 5, 2))
***** assert (icdf ("nbin", p, 5, 2), nbininv (p, 5, 2))
***** assert (icdf ("Noncentral F-Distribution", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
***** assert (icdf ("ncf", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
***** assert (icdf ("Noncentral Student T", p, 5, 2), nctinv (p, 5, 2))
***** assert (icdf ("nct", p, 5, 2), nctinv (p, 5, 2))
***** assert (icdf ("Noncentral Chi-Squared", p, 5, 2), ncx2inv (p, 5, 2))
***** assert (icdf ("ncx2", p, 5, 2), ncx2inv (p, 5, 2))
***** assert (icdf ("Normal", p, 5, 2), norminv (p, 5, 2))
***** assert (icdf ("norm", p, 5, 2), norminv (p, 5, 2))
***** assert (icdf ("Poisson", p, 5), poissinv (p, 5))
***** assert (icdf ("poiss", p, 5), poissinv (p, 5))
***** assert (icdf ("Rayleigh", p, 5), raylinv (p, 5))
***** assert (icdf ("rayl", p, 5), raylinv (p, 5))
***** assert (icdf ("Rician", p, 5, 1), riceinv (p, 5, 1))
***** assert (icdf ("rice", p, 5, 1), riceinv (p, 5, 1))
***** assert (icdf ("Student T", p, 5), tinv (p, 5))
***** assert (icdf ("t", p, 5), tinv (p, 5))
***** assert (icdf ("location-scale T", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
***** assert (icdf ("tls", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
***** assert (icdf ("Triangular", p, 5, 2, 2), triinv (p, 5, 2, 2))
***** assert (icdf ("tri", p, 5, 2, 2), triinv (p, 5, 2, 2))
***** assert (icdf ("Discrete Uniform", p, 5), unidinv (p, 5))
***** assert (icdf ("unid", p, 5), unidinv (p, 5))
***** assert (icdf ("Uniform", p, 5, 2), unifinv (p, 5, 2))
***** assert (icdf ("unif", p, 5, 2), unifinv (p, 5, 2))
***** assert (icdf ("Von Mises", p, 5, 2), vminv (p, 5, 2))
***** assert (icdf ("vm", p, 5, 2), vminv (p, 5, 2))
***** assert (icdf ("Weibull", p, 5, 2), wblinv (p, 5, 2))
***** assert (icdf ("wbl", p, 5, 2), wblinv (p, 5, 2))
***** error<icdf: distribution NAME must a char string.> icdf (1)
***** error<icdf: distribution NAME must a char string.> icdf ({"beta"})
***** error<icdf: P must be numeric.> icdf ("beta", {[1 2 3 4 5]})
***** error<icdf: P must be numeric.> icdf ("beta", "text")
***** error<icdf: values in P must be real.> icdf ("beta", 1+i)
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, "a", 2)
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, 5, "")
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, 5, {2})
***** error<icdf: chi2 distribution requires 1 parameter.> icdf ("chi2", p)
***** error<icdf: Beta distribution requires 2 parameters.> icdf ("Beta", p, 5)
***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5)
***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/dist_wrap/fitdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/fitdist.m
***** test
 x = betarnd (1, 1, 100, 1);
 pd = fitdist (x, "Beta");
 [phat, pci] = betafit (x);
 assert ([pd.a, pd.b], phat);
 assert (paramci (pd), pci);
***** test
 x1 = betarnd (1, 1, 100, 1);
 x2 = betarnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "Beta", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = betafit (x1);
 assert ([pd(1).a, pd(1).b], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = betafit (x2);
 assert ([pd(2).a, pd(2).b], phat);
 assert (paramci (pd(2)), pci);
***** test
 N = 1;
 x = binornd (N, 0.5, 100, 1);
 pd = fitdist (x, "binomial");
 [phat, pci] = binofit (sum (x), numel (x));
 assert ([pd.N, pd.p], [N, phat]);
 assert (paramci (pd), pci);
***** test
 N = 3;
 x = binornd (N, 0.4, 100, 1);
 pd = fitdist (x, "binomial", "ntrials", N);
 [phat, pci] = binofit (sum (x), numel (x) * N);
 assert ([pd.N, pd.p], [N, phat]);
 assert (paramci (pd), pci);
***** test
 N = 1;
 x1 = binornd (N, 0.5, 100, 1);
 x2 = binornd (N, 0.7, 100, 1);
 pd = fitdist ([x1; x2], "binomial", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = binofit (sum (x1), numel (x1));
 assert ([pd(1).N, pd(1).p], [N, phat]);
 assert (paramci (pd(1)), pci);
 [phat, pci] = binofit (sum (x2), numel (x2));
 assert ([pd(2).N, pd(2).p], [N, phat]);
 assert (paramci (pd(2)), pci);
***** test
 N = 5;
 x1 = binornd (N, 0.5, 100, 1);
 x2 = binornd (N, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "binomial", "ntrials", N, ...
               "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = binofit (sum (x1), numel (x1) * N);
 assert ([pd(1).N, pd(1).p], [N, phat]);
 assert (paramci (pd(1)), pci);
 [phat, pci] = binofit (sum (x2), numel (x2) * N);
 assert ([pd(2).N, pd(2).p], [N, phat]);
 assert (paramci (pd(2)), pci);
***** test
 x = bisarnd (1, 1, 100, 1);
 pd = fitdist (x, "BirnbaumSaunders");
 [phat, pci] = bisafit (x);
 assert ([pd.beta, pd.gamma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = bisarnd (1, 1, 100, 1);
 x2 = bisarnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "bisa", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = bisafit (x1);
 assert ([pd(1).beta, pd(1).gamma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = bisafit (x2);
 assert ([pd(2).beta, pd(2).gamma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = burrrnd (1, 2, 1, 100, 1);
 pd = fitdist (x, "Burr");
 [phat, pci] = burrfit (x);
 assert ([pd.alpha, pd.c, pd.k], phat);
 assert (paramci (pd), pci);
***** test
 x1 = burrrnd (1, 2, 1, 100, 1);
 x2 = burrrnd (1, 0.5, 2, 100, 1);
 pd = fitdist ([x1; x2], "burr", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = burrfit (x1);
 assert ([pd(1).alpha, pd(1).c, pd(1).k], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = burrfit (x2);
 assert ([pd(2).alpha, pd(2).c, pd(2).k], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = exprnd (1, 100, 1);
 pd = fitdist (x, "exponential");
 [muhat, muci] = expfit (x);
 assert ([pd.mu], muhat);
 assert (paramci (pd), muci);
***** test
 x1 = exprnd (1, 100, 1);
 x2 = exprnd (5, 100, 1);
 pd = fitdist ([x1; x2], "exponential", "By", [ones(100,1); 2*ones(100,1)]);
 [muhat, muci] = expfit (x1);
 assert ([pd(1).mu], muhat);
 assert (paramci (pd(1)), muci);
 [muhat, muci] = expfit (x2);
 assert ([pd(2).mu], muhat);
 assert (paramci (pd(2)), muci);
***** test
 x = evrnd (1, 1, 100, 1);
 pd = fitdist (x, "ev");
 [phat, pci] = evfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = evrnd (1, 1, 100, 1);
 x2 = evrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "extremevalue", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = evfit (x1);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = evfit (x2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = gamrnd (1, 1, 100, 1);
 pd = fitdist (x, "Gamma");
 [phat, pci] = gamfit (x);
 assert ([pd.a, pd.b], phat);
 assert (paramci (pd), pci);
***** test
 x1 = gamrnd (1, 1, 100, 1);
 x2 = gamrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "Gamma", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gamfit (x1);
 assert ([pd(1).a, pd(1).b], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = gamfit (x2);
 assert ([pd(2).a, pd(2).b], phat);
 assert (paramci (pd(2)), pci);
***** test
 rand ("seed", 4);   # for reproducibility
 x = gevrnd (-0.5, 1, 2, 1000, 1);
 pd = fitdist (x, "generalizedextremevalue");
 [phat, pci] = gevfit (x);
 assert ([pd.k, pd.sigma, pd.mu], phat);
 assert (paramci (pd), pci);
***** test
 rand ("seed", 5);   # for reproducibility
 x1 = gevrnd (-0.5, 1, 2, 1000, 1);
 rand ("seed", 9);   # for reproducibility
 x2 = gevrnd (0, 1, -4, 1000, 1);
 pd = fitdist ([x1; x2], "gev", "By", [ones(1000,1); 2*ones(1000,1)]);
 [phat, pci] = gevfit (x1);
 assert ([pd(1).k, pd(1).sigma, pd(1).mu], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = gevfit (x2);
 assert ([pd(2).k, pd(2).sigma, pd(2).mu], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = gprnd (1, 1, 1, 100, 1);
 pd = fitdist (x, "GeneralizedPareto");
 [phat, pci] = gpfit (x, 1);
 assert ([pd.k, pd.sigma, pd.theta], phat);
 assert (paramci (pd), pci);
***** test
 x = gprnd (1, 1, 2, 100, 1);
 pd = fitdist (x, "GeneralizedPareto", "theta", 2);
 [phat, pci] = gpfit (x, 2);
 assert ([pd.k, pd.sigma, pd.theta], phat);
 assert (paramci (pd), pci);
***** test
 x1 = gprnd (1, 1, 1, 100, 1);
 x2 = gprnd (0, 2, 1, 100, 1);
 pd = fitdist ([x1; x2], "gp", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gpfit (x1, 1);
 assert ([pd(1).k, pd(1).sigma, pd(1).theta], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = gpfit (x2, 1);
 assert ([pd(2).k, pd(2).sigma, pd(2).theta], phat);
 assert (paramci (pd(2)), pci);
***** test
 x1 = gprnd (3, 2, 2, 100, 1);
 x2 = gprnd (2, 3, 2, 100, 1);
 pd = fitdist ([x1; x2], "GeneralizedPareto", "theta", 2, ...
               "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gpfit (x1, 2);
 assert ([pd(1).k, pd(1).sigma, pd(1).theta], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = gpfit (x2, 2);
 assert ([pd(2).k, pd(2).sigma, pd(2).theta], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = hnrnd (0, 1, 100, 1);
 pd = fitdist (x, "HalfNormal");
 [phat, pci] = hnfit (x, 0);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x = hnrnd (1, 1, 100, 1);
 pd = fitdist (x, "HalfNormal", "mu", 1);
 [phat, pci] = hnfit (x, 1);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = hnrnd (0, 1, 100, 1);
 x2 = hnrnd (0, 2, 100, 1);
 pd = fitdist ([x1; x2], "HalfNormal", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = hnfit (x1, 0);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = hnfit (x2, 0);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x1 = hnrnd (2, 1, 100, 1);
 x2 = hnrnd (2, 2, 100, 1);
 pd = fitdist ([x1; x2], "HalfNormal", "mu", 2, ...
               "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = hnfit (x1, 2);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = hnfit (x2, 2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = invgrnd (1, 1, 100, 1);
 pd = fitdist (x, "InverseGaussian");
 [phat, pci] = invgfit (x);
 assert ([pd.mu, pd.lambda], phat);
 assert (paramci (pd), pci);
***** test
 x1 = invgrnd (1, 1, 100, 1);
 x2 = invgrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "InverseGaussian", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = invgfit (x1);
 assert ([pd(1).mu, pd(1).lambda], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = invgfit (x2);
 assert ([pd(2).mu, pd(2).lambda], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = logirnd (1, 1, 100, 1);
 pd = fitdist (x, "logistic");
 [phat, pci] = logifit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = logirnd (1, 1, 100, 1);
 x2 = logirnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "logistic", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = logifit (x1);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = logifit (x2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = loglrnd (1, 1, 100, 1);
 pd = fitdist (x, "loglogistic");
 [phat, pci] = loglfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = loglrnd (1, 1, 100, 1);
 x2 = loglrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "loglogistic", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = loglfit (x1);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = loglfit (x2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = lognrnd (1, 1, 100, 1);
 pd = fitdist (x, "lognormal");
 [phat, pci] = lognfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = lognrnd (1, 1, 100, 1);
 x2 = lognrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "lognormal", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = lognfit (x1);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = lognfit (x2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = nakarnd (2, 0.5, 100, 1);
 pd = fitdist (x, "Nakagami");
 [phat, pci] = nakafit (x);
 assert ([pd.mu, pd.omega], phat);
 assert (paramci (pd), pci);
***** test
 x1 = nakarnd (2, 0.5, 100, 1);
 x2 = nakarnd (5, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "Nakagami", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = nakafit (x1);
 assert ([pd(1).mu, pd(1).omega], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = nakafit (x2);
 assert ([pd(2).mu, pd(2).omega], phat);
 assert (paramci (pd(2)), pci);
***** test
 randp ("seed", 123);
 randg ("seed", 321);
 x = nbinrnd (2, 0.5, 100, 1);
 pd = fitdist (x, "negativebinomial");
 [phat, pci] = nbinfit (x);
 assert ([pd.R, pd.P], phat);
 assert (paramci (pd), pci);
***** test
 randp ("seed", 345);
 randg ("seed", 543);
 x1 = nbinrnd (2, 0.5, 100, 1);
 randp ("seed", 432);
 randg ("seed", 234);
 x2 = nbinrnd (5, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "nbin", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = nbinfit (x1);
 assert ([pd(1).R, pd(1).P], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = nbinfit (x2);
 assert ([pd(2).R, pd(2).P], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = normrnd (1, 1, 100, 1);
 pd = fitdist (x, "normal");
 [muhat, sigmahat, muci, sigmaci] = normfit (x);
 assert ([pd.mu, pd.sigma], [muhat, sigmahat]);
 assert (paramci (pd), [muci, sigmaci]);
***** test
 x1 = normrnd (1, 1, 100, 1);
 x2 = normrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "normal", "By", [ones(100,1); 2*ones(100,1)]);
 [muhat, sigmahat, muci, sigmaci] = normfit (x1);
 assert ([pd(1).mu, pd(1).sigma], [muhat, sigmahat]);
 assert (paramci (pd(1)), [muci, sigmaci]);
 [muhat, sigmahat, muci, sigmaci] = normfit (x2);
 assert ([pd(2).mu, pd(2).sigma], [muhat, sigmahat]);
 assert (paramci (pd(2)), [muci, sigmaci]);
***** test
 x = poissrnd (1, 100, 1);
 pd = fitdist (x, "poisson");
 [phat, pci] = poissfit (x);
 assert (pd.lambda, phat);
 assert (paramci (pd), pci);
***** test
 x1 = poissrnd (1, 100, 1);
 x2 = poissrnd (5, 100, 1);
 pd = fitdist ([x1; x2], "poisson", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = poissfit (x1);
 assert (pd(1).lambda, phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = poissfit (x2);
 assert (pd(2).lambda, phat);
 assert (paramci (pd(2)), pci);
***** test
 x = raylrnd (1, 100, 1);
 pd = fitdist (x, "rayleigh");
 [phat, pci] = raylfit (x);
 assert (pd.sigma, phat);
 assert (paramci (pd), pci);
***** test
 x1 = raylrnd (1, 100, 1);
 x2 = raylrnd (5, 100, 1);
 pd = fitdist ([x1; x2], "rayleigh", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = raylfit (x1);
 assert ( pd(1).sigma, phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = raylfit (x2);
 assert (pd(2).sigma, phat);
 assert (paramci (pd(2)), pci);
***** test
 x = ricernd (1, 1, 100, 1);
 pd = fitdist (x, "rician");
 [phat, pci] = ricefit (x);
 assert ([pd.s, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = ricernd (1, 1, 100, 1);
 x2 = ricernd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "rician", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = ricefit (x1);
 assert ([pd(1).s, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = ricefit (x2);
 assert ([pd(2).s, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** warning <fitdist: 'Stable' distribution not supported yet.> ...
 fitdist ([1 2 3 4 5], "Stable");
***** test
 x = tlsrnd (0, 1, 1, 100, 1);
 pd = fitdist (x, "tlocationscale");
 [phat, pci] = tlsfit (x);
 assert ([pd.mu, pd.sigma, pd.nu], phat);
 assert (paramci (pd), pci);
***** test
 x1 = tlsrnd (0, 1, 1, 100, 1);
 x2 = tlsrnd (5, 2, 1, 100, 1);
 pd = fitdist ([x1; x2], "tlocationscale", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = tlsfit (x1);
 assert ([pd(1).mu, pd(1).sigma, pd(1).nu], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = tlsfit (x2);
 assert ([pd(2).mu, pd(2).sigma, pd(2).nu], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = [1 2 3 4 5];
 pd = fitdist (x, "weibull");
 [phat, pci] = wblfit (x);
 assert ([pd.lambda, pd.k], phat);
 assert (paramci (pd), pci);
***** test
 x = [1 2 3 4 5 6 7 8 9 10];
 pd = fitdist (x, "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
 [phat, pci] = wblfit (x(1:5));
 assert ([pd(1).lambda, pd(1).k], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = wblfit (x(6:10));
 assert ([pd(2).lambda, pd(2).k], phat);
 assert (paramci (pd(2)), pci);
***** error <fitdist: DISTNAME is required.> fitdist (1)
***** error <fitdist: DISTNAME must be a character vector.> fitdist (1, ["as";"sd"])
***** error <fitdist: unrecognized distribution name.> fitdist (1, "some")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist (ones (2), "normal")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist ([i, 2, 3], "normal")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist (["a", "s", "d"], "normal")
***** error <fitdist: optional arguments must be in NAME-VALUE pairs.> ...
 fitdist ([1, 2, 3], "normal", "By")
***** error <fitdist: GROUPVAR argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "By", [1, 2])
***** error <fitdist: 'censoring' argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "Censoring", [1, 2])
***** error <fitdist: 'frequency' argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "frequency", [1, 2])
***** error <fitdist: 'frequency' argument must contain non-negative integer values.> ...
 fitdist ([1, 2, 3], "negativebinomial", "frequency", [1, -2, 3])
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", [1, 2])
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", i)
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", -0.5)
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", 1.5)
***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
 fitdist ([1, 2, 3], "normal", "ntrials", [1, 2])
***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
 fitdist ([1, 2, 3], "normal", "ntrials", 0)
***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 fitdist ([1, 2, 3], "normal", "options", 0)
***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 fitdist ([1, 2, 3], "normal", "options", struct ("options", 1))
***** warning fitdist ([1, 2, 3], "kernel", "kernel", "normal");
***** warning fitdist ([1, 2, 3], "kernel", "support", "positive");
***** warning fitdist ([1, 2, 3], "kernel", "width", 1);
***** error <fitdist: unknown parameter name.> ...
 fitdist ([1, 2, 3], "normal", "param", struct ("options", 1))
***** error <fitdist: must define GROUPVAR for more than one output arguments.> ...
 [pdca, gn, gl] = fitdist ([1, 2, 3], "normal");
***** error <fitdist: invalid THETA value for generalized Pareto distribution.> ...
 fitdist ([1, 2, 3], "generalizedpareto", "theta", 2);
***** error <fitdist: invalid MU value for half-normal distribution.> ...
 fitdist ([1, 2, 3], "halfnormal", "mu", 2);
77 tests, 77 passed, 0 known failure, 0 skipped
[inst/dist_wrap/pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/pdf.m
***** shared x
 x = [1:5];
***** assert (pdf ("Beta", x, 5, 2), betapdf (x, 5, 2))
***** assert (pdf ("beta", x, 5, 2), betapdf (x, 5, 2))
***** assert (pdf ("Binomial", x, 5, 2), binopdf (x, 5, 2))
***** assert (pdf ("bino", x, 5, 2), binopdf (x, 5, 2))
***** assert (pdf ("Birnbaum-Saunders", x, 5, 2), bisapdf (x, 5, 2))
***** assert (pdf ("bisa", x, 5, 2), bisapdf (x, 5, 2))
***** assert (pdf ("Burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
***** assert (pdf ("burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
***** assert (pdf ("Cauchy", x, 5, 2), cauchypdf (x, 5, 2))
***** assert (pdf ("cauchy", x, 5, 2), cauchypdf (x, 5, 2))
***** assert (pdf ("Chi-squared", x, 5), chi2pdf (x, 5))
***** assert (pdf ("chi2", x, 5), chi2pdf (x, 5))
***** assert (pdf ("Extreme Value", x, 5, 2), evpdf (x, 5, 2))
***** assert (pdf ("ev", x, 5, 2), evpdf (x, 5, 2))
***** assert (pdf ("Exponential", x, 5), exppdf (x, 5))
***** assert (pdf ("exp", x, 5), exppdf (x, 5))
***** assert (pdf ("F-Distribution", x, 5, 2), fpdf (x, 5, 2))
***** assert (pdf ("f", x, 5, 2), fpdf (x, 5, 2))
***** assert (pdf ("Gamma", x, 5, 2), gampdf (x, 5, 2))
***** assert (pdf ("gam", x, 5, 2), gampdf (x, 5, 2))
***** assert (pdf ("Geometric", x, 5), geopdf (x, 5))
***** assert (pdf ("geo", x, 5), geopdf (x, 5))
***** assert (pdf ("Generalized Extreme Value", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
***** assert (pdf ("gev", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
***** assert (pdf ("Generalized Pareto", x, 5, 2, 2), gppdf (x, 5, 2, 2))
***** assert (pdf ("gp", x, 5, 2, 2), gppdf (x, 5, 2, 2))
***** assert (pdf ("Gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
***** assert (pdf ("gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
***** assert (pdf ("Half-normal", x, 5, 2), hnpdf (x, 5, 2))
***** assert (pdf ("hn", x, 5, 2), hnpdf (x, 5, 2))
***** assert (pdf ("Hypergeometric", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
***** assert (pdf ("hyge", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
***** assert (pdf ("Inverse Gaussian", x, 5, 2), invgpdf (x, 5, 2))
***** assert (pdf ("invg", x, 5, 2), invgpdf (x, 5, 2))
***** assert (pdf ("Laplace", x, 5, 2), laplacepdf (x, 5, 2))
***** assert (pdf ("laplace", x, 5, 2), laplacepdf (x, 5, 2))
***** assert (pdf ("Logistic", x, 5, 2), logipdf (x, 5, 2))
***** assert (pdf ("logi", x, 5, 2), logipdf (x, 5, 2))
***** assert (pdf ("Log-Logistic", x, 5, 2), loglpdf (x, 5, 2))
***** assert (pdf ("logl", x, 5, 2), loglpdf (x, 5, 2))
***** assert (pdf ("Lognormal", x, 5, 2), lognpdf (x, 5, 2))
***** assert (pdf ("logn", x, 5, 2), lognpdf (x, 5, 2))
***** assert (pdf ("Nakagami", x, 5, 2), nakapdf (x, 5, 2))
***** assert (pdf ("naka", x, 5, 2), nakapdf (x, 5, 2))
***** assert (pdf ("Negative Binomial", x, 5, 2), nbinpdf (x, 5, 2))
***** assert (pdf ("nbin", x, 5, 2), nbinpdf (x, 5, 2))
***** assert (pdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
***** assert (pdf ("ncf", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
***** assert (pdf ("Noncentral Student T", x, 5, 2), nctpdf (x, 5, 2))
***** assert (pdf ("nct", x, 5, 2), nctpdf (x, 5, 2))
***** assert (pdf ("Noncentral Chi-Squared", x, 5, 2), ncx2pdf (x, 5, 2))
***** assert (pdf ("ncx2", x, 5, 2), ncx2pdf (x, 5, 2))
***** assert (pdf ("Normal", x, 5, 2), normpdf (x, 5, 2))
***** assert (pdf ("norm", x, 5, 2), normpdf (x, 5, 2))
***** assert (pdf ("Poisson", x, 5), poisspdf (x, 5))
***** assert (pdf ("poiss", x, 5), poisspdf (x, 5))
***** assert (pdf ("Rayleigh", x, 5), raylpdf (x, 5))
***** assert (pdf ("rayl", x, 5), raylpdf (x, 5))
***** assert (pdf ("Rician", x, 5, 1), ricepdf (x, 5, 1))
***** assert (pdf ("rice", x, 5, 1), ricepdf (x, 5, 1))
***** assert (pdf ("Student T", x, 5), tpdf (x, 5))
***** assert (pdf ("t", x, 5), tpdf (x, 5))
***** assert (pdf ("location-scale T", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
***** assert (pdf ("tls", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
***** assert (pdf ("Triangular", x, 5, 2, 2), tripdf (x, 5, 2, 2))
***** assert (pdf ("tri", x, 5, 2, 2), tripdf (x, 5, 2, 2))
***** assert (pdf ("Discrete Uniform", x, 5), unidpdf (x, 5))
***** assert (pdf ("unid", x, 5), unidpdf (x, 5))
***** assert (pdf ("Uniform", x, 5, 2), unifpdf (x, 5, 2))
***** assert (pdf ("unif", x, 5, 2), unifpdf (x, 5, 2))
***** assert (pdf ("Von Mises", x, 5, 2), vmpdf (x, 5, 2))
***** assert (pdf ("vm", x, 5, 2), vmpdf (x, 5, 2))
***** assert (pdf ("Weibull", x, 5, 2), wblpdf (x, 5, 2))
***** assert (pdf ("wbl", x, 5, 2), wblpdf (x, 5, 2))
***** error<pdf: distribution NAME must a char string.> pdf (1)
***** error<pdf: distribution NAME must a char string.> pdf ({"beta"})
***** error<pdf: X must be numeric.> pdf ("beta", {[1 2 3 4 5]})
***** error<pdf: X must be numeric.> pdf ("beta", "text")
***** error<pdf: values in X must be real.> pdf ("beta", 1+i)
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, "a", 2)
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, 5, "")
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, 5, {2})
***** error<pdf: chi2 distribution requires 1 parameter.> pdf ("chi2", x)
***** error<pdf: Beta distribution requires 2 parameters.> pdf ("Beta", x, 5)
***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5)
***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/dist_wrap/makedist.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/makedist.m
***** test
 pd = makedist ("beta");
 assert (class (pd), "BetaDistribution");
 assert (pd.a, 1);
 assert (pd.b, 1);
***** test
 pd = makedist ("beta", "a", 5);
 assert (pd.a, 5);
 assert (pd.b, 1);
***** test
 pd = makedist ("beta", "b", 5);
 assert (pd.a, 1);
 assert (pd.b, 5);
***** test
 pd = makedist ("beta", "a", 3, "b", 5);
 assert (pd.a, 3);
 assert (pd.b, 5);
***** test
 pd = makedist ("binomial");
 assert (class (pd), "BinomialDistribution");
 assert (pd.N, 1);
 assert (pd.p, 0.5);
***** test
 pd = makedist ("binomial", "N", 5);
 assert (pd.N, 5);
 assert (pd.p, 0.5);
***** test
 pd = makedist ("binomial", "p", 0.2);
 assert (pd.N, 1);
 assert (pd.p, 0.2);
***** test
 pd = makedist ("binomial", "N", 3, "p", 0.3);
 assert (pd.N, 3);
 assert (pd.p, 0.3);
***** test
 pd = makedist ("birnbaumsaunders");
 assert (class (pd), "BirnbaumSaundersDistribution");
 assert (pd.beta, 1);
 assert (pd.gamma, 1);
***** test
 pd = makedist ("birnbaumsaunders", "beta", 5);
 assert (pd.beta, 5);
 assert (pd.gamma, 1);
***** test
 pd = makedist ("birnbaumsaunders", "gamma", 5);
 assert (pd.beta, 1);
 assert (pd.gamma, 5);
***** test
 pd = makedist ("birnbaumsaunders", "beta", 3, "gamma", 5);
 assert (pd.beta, 3);
 assert (pd.gamma, 5);
***** test
 pd = makedist ("burr");
 assert (class (pd), "BurrDistribution");
 assert (pd.alpha, 1);
 assert (pd.c, 1);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "k", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 1);
 assert (pd.k, 5);
***** test
 pd = makedist ("burr", "c", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 5);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "alpha", 3, "c", 5);
 assert (pd.alpha, 3);
 assert (pd.c, 5);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "k", 3, "c", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 5);
 assert (pd.k, 3);
***** test
 pd = makedist ("exponential");
 assert (class (pd), "ExponentialDistribution");
 assert (pd.mu, 1);
***** test
 pd = makedist ("exponential", "mu", 5);
 assert (pd.mu, 5);
***** test
 pd = makedist ("extremevalue");
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("extremevalue", "mu", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("ev", "sigma", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("ev", "mu", -3, "sigma", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("gamma");
 assert (class (pd), "GammaDistribution");
 assert (pd.a, 1);
 assert (pd.b, 1);
***** test
 pd = makedist ("gamma", "a", 5);
 assert (pd.a, 5);
 assert (pd.b, 1);
***** test
 pd = makedist ("gamma", "b", 5);
 assert (pd.a, 1);
 assert (pd.b, 5);
***** test
 pd = makedist ("gamma", "a", 3, "b", 5);
 assert (pd.a, 3);
 assert (pd.b, 5);
***** test
 pd = makedist ("GeneralizedExtremeValue");
 assert (class (pd), "GeneralizedExtremeValueDistribution");
 assert (pd.k, 0);
 assert (pd.sigma, 1);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "k", 5);
 assert (pd.k, 5);
 assert (pd.sigma, 1);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "sigma", 5);
 assert (pd.k, 0);
 assert (pd.sigma, 5);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "k", 3, "sigma", 5);
 assert (pd.k, 3);
 assert (pd.sigma, 5);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "mu", 3, "sigma", 5);
 assert (pd.k, 0);
 assert (pd.sigma, 5);
 assert (pd.mu, 3);
***** test
 pd = makedist ("GeneralizedPareto");
 assert (class (pd), "GeneralizedParetoDistribution");
 assert (pd.k, 1);
 assert (pd.sigma, 1);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "k", 5);
 assert (pd.k, 5);
 assert (pd.sigma, 1);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "sigma", 5);
 assert (pd.k, 1);
 assert (pd.sigma, 5);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "k", 3, "sigma", 5);
 assert (pd.k, 3);
 assert (pd.sigma, 5);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "theta", 3, "sigma", 5);
 assert (pd.k, 1);
 assert (pd.sigma, 5);
 assert (pd.theta, 3);
***** test
 pd = makedist ("HalfNormal");
 assert (class (pd), "HalfNormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("HalfNormal", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("HalfNormal", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("HalfNormal", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("InverseGaussian");
 assert (class (pd), "InverseGaussianDistribution");
 assert (pd.mu, 1);
 assert (pd.lambda, 1);
***** test
 pd = makedist ("InverseGaussian", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.lambda, 1);
***** test
 pd = makedist ("InverseGaussian", "lambda", 5);
 assert (pd.mu, 1);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("InverseGaussian", "mu", 3, "lambda", 5);
 assert (pd.mu, 3);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("logistic");
 assert (class (pd), "LogisticDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("logistic", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("logistic", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("logistic", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("loglogistic");
 assert (class (pd), "LoglogisticDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("loglogistic", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("loglogistic", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("loglogistic", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Lognormal");
 assert (class (pd), "LognormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Lognormal", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Lognormal", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Lognormal", "mu", -3, "sigma", 5);
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Loguniform");
 assert (class (pd), "LoguniformDistribution");
 assert (pd.Lower, 1);
 assert (pd.Upper, 4);
***** test
 pd = makedist ("Loguniform", "Lower", 2);
 assert (pd.Lower, 2);
 assert (pd.Upper, 4);
***** test
 pd = makedist ("Loguniform", "Lower", 1, "Upper", 3);
 assert (pd.Lower, 1);
 assert (pd.Upper, 3);
***** test
 pd = makedist ("Multinomial");
 assert (class (pd), "MultinomialDistribution");
 assert (pd.Probabilities, [0.5, 0.5]);
***** test
 pd = makedist ("Multinomial", "Probabilities", [0.2, 0.3, 0.1, 0.4]);
 assert (class (pd), "MultinomialDistribution");
 assert (pd.Probabilities, [0.2, 0.3, 0.1, 0.4]);
***** test
 pd = makedist ("Nakagami");
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 1);
 assert (pd.omega, 1);
***** test
 pd = makedist ("Nakagami", "mu", 5);
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 5);
 assert (pd.omega, 1);
***** test
 pd = makedist ("Nakagami", "omega", 0.3);
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 1);
 assert (pd.omega, 0.3);
***** test
 pd = makedist ("NegativeBinomial");
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 1);
 assert (pd.P, 0.5);
***** test
 pd = makedist ("NegativeBinomial", "R", 5);
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 5);
 assert (pd.P, 0.5);
***** test
 pd = makedist ("NegativeBinomial", "p", 0.3);
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 1);
 assert (pd.P, 0.3);
***** test
 pd = makedist ("Normal");
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Normal", "mu", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Normal", "sigma", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Normal", "mu", -3, "sigma", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("PiecewiseLinear");
 assert (class (pd), "PiecewiseLinearDistribution");
 assert (pd.x, [0; 1]);
 assert (pd.Fx, [0; 1]);
***** test
 pd = makedist ("PiecewiseLinear", "x", [0, 1, 2], "Fx", [0, 0.5, 1]);
 assert (pd.x, [0; 1; 2]);
 assert (pd.Fx, [0; 0.5; 1]);
***** test
 pd = makedist ("Poisson");
 assert (class (pd), "PoissonDistribution");
 assert (pd.lambda, 1);
***** test
 pd = makedist ("Poisson", "lambda", 5);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("Rayleigh");
 assert (class (pd), "RayleighDistribution");
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rayleigh", "sigma", 5);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Rician");
 assert (class (pd), "RicianDistribution");
 assert (pd.s, 1);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rician", "s", 3);
 assert (pd.s, 3);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rician", "sigma", 3);
 assert (pd.s, 1);
 assert (pd.sigma, 3);
***** test
 pd = makedist ("Rician", "s", 2, "sigma", 3);
 assert (pd.s, 2);
 assert (pd.sigma, 3);
***** warning
 pd = makedist ("stable");
 assert (class (pd), "double");
 assert (isempty (pd), true);
***** test
 pd = makedist ("tlocationscale");
 assert (class (pd), "tLocationScaleDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "sigma", 2);
 assert (pd.mu, 0);
 assert (pd.sigma, 2);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "mu", 5, "sigma", 2);
 assert (pd.mu, 5);
 assert (pd.sigma, 2);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "nu", 1, "sigma", 2);
 assert (pd.mu, 0);
 assert (pd.sigma, 2);
 assert (pd.nu, 1);
***** test
 pd = makedist ("tlocationscale", "mu", -2, "sigma", 3, "nu", 1);
 assert (pd.mu, -2);
 assert (pd.sigma, 3);
 assert (pd.nu, 1);
***** test
 pd = makedist ("Triangular");
 assert (class (pd), "TriangularDistribution");
 assert (pd.A, 0);
 assert (pd.B, 0.5);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", -2);
 assert (pd.A, -2);
 assert (pd.B, 0.5);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", 0.5, "B", 0.9);
 assert (pd.A, 0.5);
 assert (pd.B, 0.9);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", 1, "B", 2, "C", 5);
 assert (pd.A, 1);
 assert (pd.B, 2);
 assert (pd.C, 5);
***** test
 pd = makedist ("Uniform");
 assert (class (pd), "UniformDistribution");
 assert (pd.Lower, 0);
 assert (pd.Upper, 1);
***** test
 pd = makedist ("Uniform", "Lower", -2);
 assert (pd.Lower, -2);
 assert (pd.Upper, 1);
***** test
 pd = makedist ("Uniform", "Lower", 1, "Upper", 3);
 assert (pd.Lower, 1);
 assert (pd.Upper, 3);
***** test
 pd = makedist ("Weibull");
 assert (class (pd), "WeibullDistribution");
 assert (pd.lambda, 1);
 assert (pd.k, 1);
***** test
 pd = makedist ("Weibull", "lambda", 3);
 assert (pd.lambda, 3);
 assert (pd.k, 1);
***** test
 pd = makedist ("Weibull", "lambda", 3, "k", 2);
 assert (pd.lambda, 3);
 assert (pd.k, 2);
***** error <makedist: DISTNAME must be a character vector.> makedist (1)
***** error <makedist: DISTNAME must be a character vector.> makedist (["as";"sd"])
***** error <makedist: unrecognized distribution name.> makedist ("some")
***** error <makedist: optional arguments must be in NAME-VALUE pairs.> ...
 makedist ("Beta", "a")
***** error <makedist: unknown parameter for 'Beta' distribution.> ...
 makedist ("Beta", "a", 1, "Q", 23)
***** error <makedist: unknown parameter for 'Binomial' distribution.> ...
 makedist ("Binomial", "N", 1, "Q", 23)
***** error <makedist: unknown parameter for 'BirnbaumSaunders' distribution.> ...
 makedist ("BirnbaumSaunders", "N", 1)
***** error <makedist: unknown parameter for 'Burr' distribution.> ...
 makedist ("Burr", "lambda", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'ExtremeValue' distribution.> ...
 makedist ("extremevalue", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Exponential' distribution.> ...
 makedist ("exponential", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Gamma' distribution.> ...
 makedist ("Gamma", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'GeneralizedExtremeValue' distribution.> ...
 makedist ("GeneralizedExtremeValue", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'GeneralizedPareto' distribution.> ...
 makedist ("GeneralizedPareto", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'HalfNormal' distribution.> ...
 makedist ("HalfNormal", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'InverseGaussian' distribution.> ...
 makedist ("InverseGaussian", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Logistic' distribution.> ...
 makedist ("Logistic", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Loglogistic' distribution.> ...
 makedist ("Loglogistic", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Lognormal' distribution.> ...
 makedist ("Lognormal", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Loguniform' distribution.> ...
 makedist ("Loguniform", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Multinomial' distribution.> ...
 makedist ("Multinomial", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Nakagami' distribution.> ...
 makedist ("Nakagami", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'NegativeBinomial' distribution.> ...
 makedist ("NegativeBinomial", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Normal' distribution.> ...
 makedist ("Normal", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'PiecewiseLinear' distribution.> ...
 makedist ("PiecewiseLinear", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Poisson' distribution.> ...
 makedist ("Poisson", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Rayleigh' distribution.> ...
 makedist ("Rayleigh", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Rician' distribution.> ...
 makedist ("Rician", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Stable' distribution.> ...
 makedist ("Stable", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'tLocationScale' distribution.> ...
 makedist ("tLocationScale", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Triangular' distribution.> ...
 makedist ("Triangular", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Uniform' distribution.> ...
 makedist ("Uniform", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Weibull' distribution.> ...
 makedist ("Weibull", "mu", 1, "sdfs", 34)
131 tests, 131 passed, 0 known failure, 0 skipped
[inst/dist_wrap/cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/cdf.m
***** shared x
 x = [1:5];
***** assert (cdf ("Beta", x, 5, 2), betacdf (x, 5, 2))
***** assert (cdf ("beta", x, 5, 2, "upper"), betacdf (x, 5, 2, "upper"))
***** assert (cdf ("Binomial", x, 5, 2), binocdf (x, 5, 2))
***** assert (cdf ("bino", x, 5, 2, "upper"), binocdf (x, 5, 2, "upper"))
***** assert (cdf ("Birnbaum-Saunders", x, 5, 2), bisacdf (x, 5, 2))
***** assert (cdf ("bisa", x, 5, 2, "upper"), bisacdf (x, 5, 2, "upper"))
***** assert (cdf ("Burr", x, 5, 2, 2), burrcdf (x, 5, 2, 2))
***** assert (cdf ("burr", x, 5, 2, 2, "upper"), burrcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Cauchy", x, 5, 2), cauchycdf (x, 5, 2))
***** assert (cdf ("cauchy", x, 5, 2, "upper"), cauchycdf (x, 5, 2, "upper"))
***** assert (cdf ("Chi-squared", x, 5), chi2cdf (x, 5))
***** assert (cdf ("chi2", x, 5, "upper"), chi2cdf (x, 5, "upper"))
***** assert (cdf ("Extreme Value", x, 5, 2), evcdf (x, 5, 2))
***** assert (cdf ("ev", x, 5, 2, "upper"), evcdf (x, 5, 2, "upper"))
***** assert (cdf ("Exponential", x, 5), expcdf (x, 5))
***** assert (cdf ("exp", x, 5, "upper"), expcdf (x, 5, "upper"))
***** assert (cdf ("F-Distribution", x, 5, 2), fcdf (x, 5, 2))
***** assert (cdf ("f", x, 5, 2, "upper"), fcdf (x, 5, 2, "upper"))
***** assert (cdf ("Gamma", x, 5, 2), gamcdf (x, 5, 2))
***** assert (cdf ("gam", x, 5, 2, "upper"), gamcdf (x, 5, 2, "upper"))
***** assert (cdf ("Geometric", x, 5), geocdf (x, 5))
***** assert (cdf ("geo", x, 5, "upper"), geocdf (x, 5, "upper"))
***** assert (cdf ("Generalized Extreme Value", x, 5, 2, 2), gevcdf (x, 5, 2, 2))
***** assert (cdf ("gev", x, 5, 2, 2, "upper"), gevcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Generalized Pareto", x, 5, 2, 2), gpcdf (x, 5, 2, 2))
***** assert (cdf ("gp", x, 5, 2, 2, "upper"), gpcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Gumbel", x, 5, 2), gumbelcdf (x, 5, 2))
***** assert (cdf ("gumbel", x, 5, 2, "upper"), gumbelcdf (x, 5, 2, "upper"))
***** assert (cdf ("Half-normal", x, 5, 2), hncdf (x, 5, 2))
***** assert (cdf ("hn", x, 5, 2, "upper"), hncdf (x, 5, 2, "upper"))
***** assert (cdf ("Hypergeometric", x, 5, 2, 2), hygecdf (x, 5, 2, 2))
***** assert (cdf ("hyge", x, 5, 2, 2, "upper"), hygecdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Inverse Gaussian", x, 5, 2), invgcdf (x, 5, 2))
***** assert (cdf ("invg", x, 5, 2, "upper"), invgcdf (x, 5, 2, "upper"))
***** assert (cdf ("Laplace", x, 5, 2), laplacecdf (x, 5, 2))
***** assert (cdf ("laplace", x, 5, 2, "upper"), laplacecdf (x, 5, 2, "upper"))
***** assert (cdf ("Logistic", x, 5, 2), logicdf (x, 5, 2))
***** assert (cdf ("logi", x, 5, 2, "upper"), logicdf (x, 5, 2, "upper"))
***** assert (cdf ("Log-Logistic", x, 5, 2), loglcdf (x, 5, 2))
***** assert (cdf ("logl", x, 5, 2, "upper"), loglcdf (x, 5, 2, "upper"))
***** assert (cdf ("Lognormal", x, 5, 2), logncdf (x, 5, 2))
***** assert (cdf ("logn", x, 5, 2, "upper"), logncdf (x, 5, 2, "upper"))
***** assert (cdf ("Nakagami", x, 5, 2), nakacdf (x, 5, 2))
***** assert (cdf ("naka", x, 5, 2, "upper"), nakacdf (x, 5, 2, "upper"))
***** assert (cdf ("Negative Binomial", x, 5, 2), nbincdf (x, 5, 2))
***** assert (cdf ("nbin", x, 5, 2, "upper"), nbincdf (x, 5, 2, "upper"))
***** assert (cdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfcdf (x, 5, 2, 2))
***** assert (cdf ("ncf", x, 5, 2, 2, "upper"), ncfcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Noncentral Student T", x, 5, 2), nctcdf (x, 5, 2))
***** assert (cdf ("nct", x, 5, 2, "upper"), nctcdf (x, 5, 2, "upper"))
***** assert (cdf ("Noncentral Chi-Squared", x, 5, 2), ncx2cdf (x, 5, 2))
***** assert (cdf ("ncx2", x, 5, 2, "upper"), ncx2cdf (x, 5, 2, "upper"))
***** assert (cdf ("Normal", x, 5, 2), normcdf (x, 5, 2))
***** assert (cdf ("norm", x, 5, 2, "upper"), normcdf (x, 5, 2, "upper"))
***** assert (cdf ("Poisson", x, 5), poisscdf (x, 5))
***** assert (cdf ("poiss", x, 5, "upper"), poisscdf (x, 5, "upper"))
***** assert (cdf ("Rayleigh", x, 5), raylcdf (x, 5))
***** assert (cdf ("rayl", x, 5, "upper"), raylcdf (x, 5, "upper"))
***** assert (cdf ("Rician", x, 5, 1), ricecdf (x, 5, 1))
***** assert (cdf ("rice", x, 5, 1, "upper"), ricecdf (x, 5, 1, "upper"))
***** assert (cdf ("Student T", x, 5), tcdf (x, 5))
***** assert (cdf ("t", x, 5, "upper"), tcdf (x, 5, "upper"))
***** assert (cdf ("location-scale T", x, 5, 1, 2), tlscdf (x, 5, 1, 2))
***** assert (cdf ("tls", x, 5, 1, 2, "upper"), tlscdf (x, 5, 1, 2, "upper"))
***** assert (cdf ("Triangular", x, 5, 2, 2), tricdf (x, 5, 2, 2))
***** assert (cdf ("tri", x, 5, 2, 2, "upper"), tricdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Discrete Uniform", x, 5), unidcdf (x, 5))
***** assert (cdf ("unid", x, 5, "upper"), unidcdf (x, 5, "upper"))
***** assert (cdf ("Uniform", x, 5, 2), unifcdf (x, 5, 2))
***** assert (cdf ("unif", x, 5, 2, "upper"), unifcdf (x, 5, 2, "upper"))
***** assert (cdf ("Von Mises", x, 5, 2), vmcdf (x, 5, 2))
***** assert (cdf ("vm", x, 5, 2, "upper"), vmcdf (x, 5, 2, "upper"))
***** assert (cdf ("Weibull", x, 5, 2), wblcdf (x, 5, 2))
***** assert (cdf ("wbl", x, 5, 2, "upper"), wblcdf (x, 5, 2, "upper"))
***** error<cdf: distribution NAME must a char string.> cdf (1)
***** error<cdf: distribution NAME must a char string.> cdf ({"beta"})
***** error<cdf: X must be numeric.> cdf ("beta", {[1 2 3 4 5]})
***** error<cdf: X must be numeric.> cdf ("beta", "text")
***** error<cdf: values in X must be real.> cdf ("beta", 1+i)
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, "a", 2)
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, 5, "")
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, 5, {2})
***** error<cdf: chi2 distribution requires 1 parameter.> cdf ("chi2", x)
***** error<cdf: Beta distribution requires 2 parameters.> cdf ("Beta", x, 5)
***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5)
***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/dist_wrap/random.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/random.m
***** assert (size (random ("Beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
***** assert (size (random ("beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
***** assert (size (random ("Binomial", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
***** assert (size (random ("bino", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
***** assert (size (random ("Birnbaum-Saunders", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
***** assert (size (random ("bisa", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
***** assert (size (random ("Burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
***** assert (size (random ("cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
***** assert (size (random ("Chi-squared", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
***** assert (size (random ("chi2", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
***** assert (size (random ("Extreme Value", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
***** assert (size (random ("ev", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
***** assert (size (random ("Exponential", 5, [10, 20])), size (exprnd (5, 10, 20)))
***** assert (size (random ("exp", 5, [10, 20])), size (exprnd (5, 10, 20)))
***** assert (size (random ("F-Distribution", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
***** assert (size (random ("f", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
***** assert (size (random ("Gamma", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
***** assert (size (random ("gam", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
***** assert (size (random ("Geometric", 5, [10, 20])), size (geornd (5, 10, 20)))
***** assert (size (random ("geo", 5, [10, 20])), size (geornd (5, 10, 20)))
***** assert (size (random ("Generalized Extreme Value", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("gev", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Generalized Pareto", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
***** assert (size (random ("gp", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
***** assert (size (random ("gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
***** assert (size (random ("Half-normal", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
***** assert (size (random ("hn", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
***** assert (size (random ("Hypergeometric", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
***** assert (size (random ("hyge", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
***** assert (size (random ("Inverse Gaussian", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
***** assert (size (random ("invg", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
***** assert (size (random ("Laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
***** assert (size (random ("laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
***** assert (size (random ("Logistic", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
***** assert (size (random ("logi", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
***** assert (size (random ("Log-Logistic", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
***** assert (size (random ("logl", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
***** assert (size (random ("Lognormal", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
***** assert (size (random ("logn", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
***** assert (size (random ("Nakagami", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
***** assert (size (random ("naka", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
***** assert (size (random ("Negative Binomial", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
***** assert (size (random ("nbin", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
***** assert (size (random ("Noncentral F-Distribution", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("ncf", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Noncentral Student T", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
***** assert (size (random ("nct", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
***** assert (size (random ("Noncentral Chi-Squared", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
***** assert (size (random ("ncx2", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
***** assert (size (random ("Normal", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
***** assert (size (random ("norm", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
***** assert (size (random ("Poisson", 5, [10, 20])), size (poissrnd (5, 10, 20)))
***** assert (size (random ("poiss", 5, [10, 20])), size (poissrnd (5, 10, 20)))
***** assert (size (random ("Rayleigh", 5, [10, 20])), size (raylrnd (5, 10, 20)))
***** assert (size (random ("rayl", 5, [10, 20])), size (raylrnd (5, 10, 20)))
***** assert (size (random ("Rician", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
***** assert (size (random ("rice", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
***** assert (size (random ("Student T", 5, [10, 20])), size (trnd (5, 10, 20)))
***** assert (size (random ("t", 5, [10, 20])), size (trnd (5, 10, 20)))
***** assert (size (random ("location-scale T", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
***** assert (size (random ("tls", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
***** assert (size (random ("Triangular", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
***** assert (size (random ("tri", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Discrete Uniform", 5, [10, 20])), size (unidrnd (5, 10, 20)))
***** assert (size (random ("unid", 5, [10, 20])), size (unidrnd (5, 10, 20)))
***** assert (size (random ("Uniform", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
***** assert (size (random ("unif", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
***** assert (size (random ("Von Mises", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
***** assert (size (random ("vm", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
***** assert (size (random ("Weibull", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
***** assert (size (random ("wbl", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
***** error<random: distribution NAME must a char string.> random (1)
***** error<random: distribution NAME must a char string.> random ({"beta"})
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", "a", 2)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "")
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, {2})
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", "a", 2, 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "", 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, {2}, 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "", 2, 10)
***** error<random: chi2 distribution requires 1 parameter.> random ("chi2")
***** error<random: Beta distribution requires 2 parameters.> random ("Beta", 5)
***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5)
***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5, 2)
87 tests, 87 passed, 0 known failure, 0 skipped
[inst/friedman.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/friedman.m
***** demo
 load popcorn;
 friedman (popcorn, 3);
***** demo
 load popcorn;
 [p, atab] = friedman (popcorn, 3, "off");
 disp (p);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab] = friedman (popcorn, 3, "off");
 assert (p, 0.001028853354594794, 1e-14);
 assert (atab{2,2}, 99.75, 1e-14);
 assert (atab{2,3}, 2, 0);
 assert (atab{2,4}, 49.875, 1e-14);
 assert (atab{2,5}, 13.75862068965517, 1e-14);
 assert (atab{2,6}, 0.001028853354594794, 1e-14);
 assert (atab{3,2}, 0.08333333333333215, 1e-14);
 assert (atab{3,4}, 0.04166666666666607, 1e-14);
 assert (atab{4,3}, 12, 0);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab, stats] = friedman (popcorn, 3, "off");
 assert (atab{5,2}, 116, 0);
 assert (atab{5,3}, 17, 0);
 assert (stats.source, "friedman");
 assert (stats.n, 2);
 assert (stats.meanranks, [8, 4.75, 2.25], 0);
 assert (stats.sigma, 2.692582403567252, 1e-14);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/jackknife.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/jackknife.m
***** demo
 for k = 1:1000
   rand ("seed", k);  # for reproducibility
   x = rand (10, 1);
   s(k) = std (x);
   jackstat = jackknife (@std, x);
   j(k) = 10 * std (x) - 9 * mean (jackstat);
 endfor
 figure();
 hist ([s', j'], 0:sqrt(1/12)/10:2*sqrt(1/12))
***** demo
 for k = 1:1000
   randn ("seed", k); # for reproducibility
   x = randn (1, 50);
   rand ("seed", k);  # for reproducibility
   y = rand (1, 50);
   jackstat = jackknife (@(x) std(x{1})/std(x{2}), y, x);
   j(k) = 50 * std (y) / std (x) - 49 * mean (jackstat);
   v(k) = sumsq ((50 * std (y) / std (x) - 49 * jackstat) - j(k)) / (50 * 49);
 endfor
 t = (j - sqrt (1 / 12)) ./ sqrt (v);
 figure();
 plot (sort (tcdf (t, 49)), ...
       "-;Almost linear mapping indicates good fit with t-distribution.;")
***** test
 ##Example from Quenouille, Table 1
 d=[0.18 4.00 1.04 0.85 2.14 1.01 3.01 2.33 1.57 2.19];
 jackstat = jackknife ( @(x) 1/mean(x), d );
 assert ( 10 / mean(d) - 9 * mean(jackstat), 0.5240, 1e-5 );
1 test, 1 passed, 0 known failure, 0 skipped
[inst/qqplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/qqplot.m
***** test
 hf = figure ("visible", "off");
 unwind_protect
   qqplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
warning: using the gnuplot graphics toolkit is discouraged

The gnuplot graphics toolkit is not actively maintained and has a number
of limitations that are unlikely to be fixed.  Communication with gnuplot
uses a one-directional pipe and limited information is passed back to the
Octave interpreter so most changes made interactively in the plot window
will not be reflected in the graphics properties managed by Octave.  For
example, if the plot window is closed with a mouse click, Octave will not
be notified and will not update its internal list of open figure windows.
The qt toolkit is recommended instead.
***** error qqplot ()
***** error <qqplot: X must be a numeric vector> qqplot ({1})
***** error <qqplot: X must be a numeric vector> qqplot (ones (2,2))
***** error <qqplot: X must be a numeric vecto> qqplot (1, "foobar")
***** error <qqplot: no inverse CDF found> qqplot ([1 2 3], "foobar")
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/sigma_pts.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/sigma_pts.m
***** demo
 K      = [1 0.5; 0.5 1]; # covaraince matrix
 # calculate and build associated ellipse
 [R,S,~] = svd (K);
 theta   = atan2 (R(2,1), R(1,1));
 v       = sqrt (diag (S));
 v       = v .* [cos(theta) sin(theta); -sin(theta) cos(theta)];
 t       = linspace (0, 2*pi, 100).';
 xe      = v(1,1) * cos (t) + v(2,1) * sin (t);
 ye      = v(1,2) * cos (t) + v(2,2) * sin (t);

 figure(1); clf; hold on
 # Plot ellipse and axes
 line ([0 0; v(:,1).'],[0 0; v(:,2).'])
 plot (xe,ye,'-r');

 col = 'rgb';
 l     = [-1.8 -1 1.5];
 for li = 1:3
  p     = sigma_pts (2, [], K, l(li));
  tmp   = plot (p(2:end,1), p(2:end,2), ['x' col(li)], ...
               p(1,1), p(1,2), ['o' col(li)]);
  h(li) = tmp(1);
 endfor
 hold off
 axis image
 legend (h, arrayfun (@(x) sprintf ("l:%.2g", x), l, "unif", 0));
***** test
 p = sigma_pts (5);
 assert (mean (p), zeros(1,5), sqrt(eps));
 assert (cov (p), eye(5), sqrt(eps));
***** test
 m = randn(1, 5);
 p = sigma_pts (5, m);
 assert (mean (p), m, sqrt(eps));
 assert (cov (p), eye(5), sqrt(eps));
***** test
 x = linspace (0,1,5);
 K = exp (- (x.' - x).^2/ 0.5);
 p = sigma_pts (5, [], K);
 assert (mean (p), zeros(1,5), sqrt(eps));
 assert (cov (p), K, sqrt(eps));
***** error sigma_pts(2,1);
***** error sigma_pts(2,[],1);
***** error sigma_pts(2,1,1);
***** error sigma_pts(2,[0.5 0.5],[-1 0; 0 0]);
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/mahal.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/mahal.m
***** error mahal ()
***** error mahal (1, 2, 3)
***** error mahal ("A", "B")
***** error <must be numeric> mahal ([1, 2], ["A", "B"])
***** error mahal (ones (2, 2, 2))
***** error <must be 2-D matrices> mahal (ones (2, 2), ones (2, 2, 2))
***** error <same number of columns> mahal (ones (2, 2), ones (2, 3))
***** test
 X = [1 0; 0 1; 1 1; 0 0];
 assert (mahal (X, X), [1.5; 1.5; 1.5; 1.5], 10*eps)
 assert (mahal (X, X+1), [7.5; 7.5; 1.5; 13.5], 10*eps)
***** assert (mahal ([true; true], [false; true]), [0.5; 0.5], eps)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/standardizeMissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/standardizeMissing.m
***** assert (standardizeMissing (1, 1), NaN)
***** assert (standardizeMissing (1, 0), 1)
***** assert (standardizeMissing (eye(2), 1), [NaN 0;0 NaN])
***** assert (standardizeMissing ([1:3;4:6], [2 3; 4 5]), [1, NaN, NaN; NaN, NaN, 6])
***** assert (standardizeMissing (cat (3,1,2,3,4), 3), cat (3,1,2,NaN,4))
***** assert (standardizeMissing ('foo', 'a'), 'foo')
***** assert (standardizeMissing ('foo', 'f'), ' oo')
***** assert (standardizeMissing ('foo', 'o'), 'f  ')
***** assert (standardizeMissing ('foo', 'oo'), 'f  ')
***** assert (standardizeMissing ({'foo'}, 'f'), {'foo'})
***** assert (standardizeMissing ({'foo'}, {'f'}), {'foo'})
***** assert (standardizeMissing ({'foo'}, 'test'), {'foo'})
***** assert (standardizeMissing ({'foo'}, {'test'}), {'foo'})
***** assert (standardizeMissing ({'foo'}, 'foo'), {''})
***** assert (standardizeMissing ({'foo'}, {'foo'}), {''})
***** assert (standardizeMissing (['foo';'bar'], 'oar'), ['f  ';'b  '])
***** assert (standardizeMissing (['foo';'bar'], ['o';'a';'r']), ['f  ';'b  '])
***** assert (standardizeMissing (['foo';'bar'], ['o ';'ar']), ['f  ';'b  '])
***** assert (standardizeMissing ({'foo','bar'}, 'foo'), {'','bar'})
***** assert (standardizeMissing ({'foo','bar'}, 'f'), {'foo','bar'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
***** assert (standardizeMissing ({'foo'}, {'f', 'oo'}), {'foo'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo'}), {'','bar'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
***** assert (standardizeMissing (double (1), single (1)), double (NaN))
***** assert (standardizeMissing (single (1), single (1)), single (NaN))
***** assert (standardizeMissing (single (1), double (1)), single (NaN))
***** assert (standardizeMissing (single (1), true), single (NaN))
***** assert (standardizeMissing (double (1), int32(1)), double (NaN))
***** assert (standardizeMissing (true, true), true)
***** assert (standardizeMissing (true, 1), true)
***** assert (standardizeMissing (int32 (1), int32 (1)), int32 (1))
***** assert (standardizeMissing (int32 (1), 1), int32 (1))
***** assert (standardizeMissing (uint32 (1), uint32 (1)), uint32 (1))
***** assert (standardizeMissing (uint32 (1), 1), uint32 (1))
***** error standardizeMissing ();
***** error standardizeMissing (1);
***** error standardizeMissing (1,2,3);
***** error <only cells of strings> standardizeMissing ({'abc', 1}, 1);
***** error <unsupported data type> standardizeMissing (struct ('a','b'), 1);
***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], {1});
***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], 'a');
***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], struct ('a', 1));
***** error <'indicator' and 'A' must have > standardizeMissing ('foo', 1);
***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {1});
***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {'f'});
***** error <'indicator' and 'A' must have > standardizeMissing ('foo', struct ('a', 1));
***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
49 tests, 49 passed, 0 known failure, 0 skipped
[inst/violin.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/violin.m
***** demo
 clf
 x = zeros (9e2, 10);
 for i=1:10
   x(:,i) = (0.1 * randn (3e2, 3) * (randn (3,1) + 1) + 2 * randn (1,3))(:);
 endfor
 h = violin (x, "color", "c");
 axis tight
 set (h.violin, "linewidth", 2);
 set (gca, "xgrid", "on");
 xlabel ("Variables")
 ylabel ("Values")
***** demo
 clf
 data = {randn(100,1)*5+140, randn(130,1)*8+135};
 subplot (1,2,1)
 title ("Grade 3 heights - vertical");
 set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
 violin (data, "Nbins", 10);
 axis tight

 subplot(1,2,2)
 title ("Grade 3 heights - horizontal");
 set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
 violin (data, "horizontal", "Nbins", 10);
 axis tight
***** demo
 clf
 data = exprnd (0.1, 500,4);
 violin (data, "nbins", {5,10,50,100});
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = exprnd (0.1, 500,4);
 violin (data, "color", jet(4));
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = repmat(exprnd (0.1, 500,1), 1, 4);
 violin (data, "width", linspace (0.1,0.5,4));
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = repmat(exprnd (0.1, 500,1), 1, 4);
 violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
 axis ([0 5 0 max(data(:))])
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = exprnd (0.1, 500,4);
   violin (data, "color", jet(4));
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = {randn(100,1)*5+140, randn(130,1)*8+135};
   subplot (1,2,1)
   title ("Grade 3 heights - vertical");
   set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
   violin (data, "Nbins", 10);
   axis tight
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = {randn(100,1)*5+140, randn(130,1)*8+135};
   subplot (1,2,1)
   title ("Grade 3 heights - vertical");
   set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
   violin (data, "Nbins", 10);
   axis tight
   subplot(1,2,2)
   title ("Grade 3 heights - horizontal");
   set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
   violin (data, "horizontal", "Nbins", 10);
   axis tight
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = repmat(exprnd (0.1, 500,1), 1, 4);
   violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = repmat(exprnd (0.1, 500,1), 1, 4);
   violin (data, "width", linspace (0.1,0.5,4));
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/fullfact.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fullfact.m
***** demo
 ## Full factorial design with 3 binary variables
 fullfact (3)
***** demo
 ## Full factorial design with 3 ordinal variables
 fullfact ([2, 3, 4])
***** error fullfact ();
***** error fullfact (2, 5);
***** error fullfact (2.5);
***** error fullfact (0);
***** error fullfact (-3);
***** error fullfact (3+2i);
***** error fullfact (Inf);
***** error fullfact (NaN);
***** error fullfact ([1, 2, -3]);
***** error fullfact ([0, 1, 2]);
***** error fullfact ([1, 2, NaN]);
***** error fullfact ([1, 2, Inf]);
***** test
 A = fullfact (2);
 assert (A, [0, 0; 0, 1; 1, 0; 1, 1]);
***** test
 A = fullfact ([1, 2]);
 assert (A, [1, 1; 1, 2]);
***** test
 A = fullfact ([1, 2, 4]);
 A_out = [1, 1, 1; 1, 1, 2; 1, 1, 3; 1, 1, 4; ...
          1, 2, 1; 1, 2, 2; 1, 2, 3; 1, 2, 4];
 assert (A, A_out);
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationSVM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/CompactClassificationSVM.m
***** demo
 ## Create a support vectors machine classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcsvm (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl);

 whos ('Mdl', 'CMdl')
***** error<CompactClassificationSVM: invalid classification object.> ...
 CompactClassificationSVM (1)
***** shared x, y, CMdl
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
***** test
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 assert (isempty (CMdl.Alpha), true)
 assert (sum (CMdl.IsSupportVector), numel (CMdl.Beta))
 [label, score] = predict (CMdl, xc);
 assert (label, [1; 2; 2]);
 assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
 assert (score(:,1), -score(:,2), eps)
***** test
 Mdl = fitcsvm (x, y);
 CMdl = compact (Mdl);
 assert (isempty (CMdl.Beta), true)
 assert (sum (CMdl.IsSupportVector), numel (CMdl.Alpha))
 assert (numel (CMdl.Alpha), 24)
 assert (CMdl.Bias, -14.415, 1e-3)
 xc = [min(x); mean(x); max(x)];
 label = predict (CMdl, xc);
 assert (label, [1; 2; 2]);
***** error<CompactClassificationSVM.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationSVM.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationSVM.predict: XC must have the same number of predictors as the trained SVM model.> ...
 predict (CMdl, 1)
***** test
 CMdl.ScoreTransform = "a";
***** error<CompactClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (CMdl, x);
***** test
 rand ("seed", 1);
 C = cvpartition (y, 'HoldOut', 0.15);
 Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
                'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 testInds = test (C);
 expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
                    2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
                   -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
 computed_margin = margin (CMdl, x(testInds,:), y(testInds,:));
 assert (computed_margin, expected_margin, 1e-4);
***** error<CompactClassificationSVM.margin: too few input arguments.> ...
 margin (CMdl)
***** error<CompactClassificationSVM.margin: too few input arguments.> ...
 margin (CMdl, zeros (2))
***** error<CompactClassificationSVM.margin: X is empty.> ...
 margin (CMdl, [], 1)
***** error<CompactClassificationSVM.margin: X must have the same number of predictors as the trained SVM model.> ...
 margin (CMdl, 1, 1)
***** error<CompactClassificationSVM.margin: Y is empty.> ...
 margin (CMdl, [1, 2], [])
***** error<CompactClassificationSVM.margin: Y must have the same number of rows as X.> ...
 margin (CMdl, [1, 2], [1; 2])
***** test
 rand ("seed", 1);
 C = cvpartition (y, 'HoldOut', 0.15);
 Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
                'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 testInds = test (C);
 L1 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
 L2 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
 L3 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
 L4 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
 L5 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
 L6 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
 assert (L1, 2.8711, 1e-4);
 assert (L2, 0.5333, 1e-4);
 assert (L3, 10.9685, 1e-4);
 assert (L4, 1.9827, 1e-4);
 assert (L5, 1.5849, 1e-4);
 assert (L6, 7.6739, 1e-4);
***** error<CompactClassificationSVM.loss: too few input arguments.> ...
 loss (CMdl)
***** error<CompactClassificationSVM.loss: too few input arguments.> ...
 loss (CMdl, zeros (2))
***** error<CompactClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
 loss (CMdl, [1, 2], 1, "LossFun")
***** error<CompactClassificationSVM.loss: X is empty.> ...
 loss (CMdl, [], zeros (2))
***** error<CompactClassificationSVM.loss: X must have the same number of predictors as the trained SVM model.> ...
 loss (CMdl, 1, zeros (2))
***** error<CompactClassificationSVM.loss: Y is empty.> ...
 loss (CMdl, [1, 2], [])
***** error<CompactClassificationSVM.loss: Y must have the same number of rows as X.> ...
 loss (CMdl, [1, 2], [1; 2])
***** error<CompactClassificationSVM.loss: 'LossFun' must be a character vector.> ...
 loss (CMdl, [1, 2], 1, "LossFun", 1)
***** error<CompactClassificationSVM.loss: unsupported Loss function.> ...
 loss (CMdl, [1, 2], 1, "LossFun", "some")
***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (CMdl, [1, 2], 1, "Weights", ['a', 'b'])
***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (CMdl, [1, 2], 1, "Weights", 'a')
***** error<CompactClassificationSVM.loss: size of 'Weights' must be equal to the number of rows in X.> ...
 loss (CMdl, [1, 2], 1, "Weights", [1, 2])
***** error<CompactClassificationSVM.loss: invalid parameter name in optional pair arguments.> ...
 loss (CMdl, [1, 2], 1, "some", "some")
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationPartitionedModel.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationPartitionedModel.m
***** demo

 load fisheriris
 x = meas;
 y = species;

 ## Create a KNN classifier model
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create a partition for 5-fold cross-validation
 partition = cvpartition (y, "KFold", 5);

 ## Create the ClassificationPartitionedModel object
 cvModel = crossval (obj, 'cvPartition', partition)
***** demo

 load fisheriris
 x = meas;
 y = species;

 ## Create a KNN classifier model
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create the ClassificationPartitionedModel object
 cvModel = crossval (obj);

 ## Predict the class labels for the observations not used for training
 [label, score, cost] = kfoldPredict (cvModel);
 fprintf ("Cross-validated accuracy = %1.2f%% (%d/%d)\n", ...
          sum (strcmp (label, y)) / numel (y) *100, ...
          sum (strcmp (label, y)), numel (y))
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.3);
 cvModel = crossval (a, "KFold", 5);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 150);
 assert (numel (cvModel.Trained), 5);
 assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
 assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
 assert (cvModel.KFold, 5);
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
 cvModel = crossval (a, "HoldOut", 0.3);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {meas, species});
 assert (cvModel.NumObservations, 150);
 assert (numel (cvModel.Trained), 1);
 assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
 assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcgam (x, y, "Interactions", "all");
 cvModel = crossval (a, "KFold", 5);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 5);
 assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
 assert (cvModel.CrossValidatedModel, "ClassificationGAM");
 assert (cvModel.KFold, 5);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcgam (x, y);
 cvModel = crossval (a, "LeaveOut", "on");
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 4);
 assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
 assert (cvModel.CrossValidatedModel, "ClassificationGAM");
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 partition = cvpartition (y, "KFold", 5);
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, 1);
 assert (cvModel.ModelParameters.NSMethod, "kdtree");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "NSMethod", "exhaustive");
 partition = cvpartition (y, "HoldOut", 0.2);
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, 1);
 assert (cvModel.ModelParameters.NSMethod, "exhaustive");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 3;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 partition = cvpartition (y, "LeaveOut");
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, k);
 assert (cvModel.ModelParameters.NSMethod, "kdtree");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcnet (x, y, "IterationLimit", 50);
 cvModel = crossval (a, "KFold", 5);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 5);
 assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
 assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
 assert (cvModel.KFold, 5);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcnet (x, y, "LayerSizes", [5, 3]);
 cvModel = crossval (a, "LeaveOut", "on");
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 4);
 assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
 assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 SVMModel = fitcsvm (x,y);
 CVMdl = crossval (SVMModel, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** error<ClassificationPartitionedModel: too few input arguments.> ...
 ClassificationPartitionedModel ()
***** error<ClassificationPartitionedModel: too few input arguments.> ...
 ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ones (4,1)))
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
 cvModel = crossval (a, "Kfold", 4);
 [label, score, cost] = kfoldPredict (cvModel);
 assert (class(cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {meas, species});
 assert (cvModel.NumObservations, 150);
***** # assert (label, {"b"; "b"; "a"; "a"});
***** # assert (score, [4.5380e-01, 5.4620e-01; 2.4404e-01, 7.5596e-01; ...
***** #         9.9392e-01, 6.0844e-03; 9.9820e-01, 1.8000e-03], 1e-4);
***** # assert (cost, [5.4620e-01, 4.5380e-01; 7.5596e-01, 2.4404e-01; ...
***** #         6.0844e-03, 9.9392e-01; 1.8000e-03, 9.9820e-01], 1e-4);
***** test
 x = ones(4, 11);
 y = {"a"; "a"; "b"; "b"};
 k = 3;
 a = fitcknn (x, y, "NumNeighbors", k);
 partition = cvpartition (y, "LeaveOut");
 cvModel = ClassificationPartitionedModel (a, partition);
 [label, score, cost] = kfoldPredict (cvModel);
 assert (class(cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, k);
 assert (cvModel.ModelParameters.NSMethod, "exhaustive");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
 assert (label, {"b"; "b"; "a"; "a"});
 assert (score, [0.3333, 0.6667; 0.3333, 0.6667; 0.6667, 0.3333; ...
          0.6667, 0.3333], 1e-4);
 assert (cost, [0.6667, 0.3333; 0.6667, 0.3333; 0.3333, 0.6667; ...
          0.3333, 0.6667], 1e-4);
***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationSVM cross validated models.> ...
 [label, score, cost] = kfoldPredict (crossval (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1))))
***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationNeuralNetwork cross validated models.> ...
 [label, score, cost] = kfoldPredict (crossval (ClassificationNeuralNetwork (ones (40,2), randi ([1, 2], 40, 1))))
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/Classification/ConfusionMatrixChart.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ConfusionMatrixChart.m
***** demo
 ## Create a simple ConfusionMatrixChart Object

 cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"})
 NormalizedValues = cm.NormalizedValues
 ClassLabels = cm.ClassLabels
***** test
 hf = figure ("visible", "off");
 unwind_protect
   cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"});
   assert (isa (cm, "ConfusionMatrixChart"), true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationDiscriminant.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationDiscriminant.m
***** demo
 ## Create discriminant classifier
 ## Evaluate some model predictions on new data.

 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 obj = fitcdiscr (x, y);
 [label, score, cost] = predict (obj, xc);
***** demo
 load fisheriris
 model = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 ## Compute loss for discriminant model
 L = loss (model, X, Y)
***** demo
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 ## Margin for discriminant model
 m = margin (mdl, X, Y)
***** demo
 load fisheriris
 x = meas;
 y = species;
 obj = fitcdiscr (x, y, "gamma", 0.4);
 ## Cross-validation for discriminant model
 CVMdl = crossval (obj)
***** test
 load fisheriris
 x = meas;
 y = species;
 PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
 Mdl = ClassificationDiscriminant (x, y, "PredictorNames", PredictorNames);
 sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
          0.092721, 0.115388, 0.055244, 0.032710; ...
          0.167514, 0.055244, 0.185188, 0.042665; ...
          0.038401, 0.032710, 0.042665, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0, 0}, 1e-15)
 assert (Mdl.ClassNames, unique (species))
 assert (Mdl.Sigma, sigma, 1e-6)
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (Mdl.LogDetSigma, -9.9585, 1e-4)
 assert (Mdl.PredictorNames, PredictorNames)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = ClassificationDiscriminant (x, y, "Gamma", 0.5);
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
 assert (Mdl.ClassNames, unique (species))
 assert (Mdl.Sigma, sigma, 1e-6)
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (Mdl.LogDetSigma, -8.6884, 1e-4)
***** shared X, Y, MODEL
 X = rand (10,2);
 Y = [ones(5,1);2*ones(5,1)];
 MODEL = ClassificationDiscriminant (X, Y);
***** error<ClassificationDiscriminant: too few input arguments.> ClassificationDiscriminant ()
***** error<ClassificationDiscriminant: too few input arguments.> ...
 ClassificationDiscriminant (ones(4, 1))
***** error<ClassificationDiscriminant: number of rows in X and Y must be equal.> ...
 ClassificationDiscriminant (ones (4,2), ones (1,4))
***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", ["A"])
***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", "A")
***** error<ClassificationDiscriminant: 'PredictorNames' must equal the number of columns in X.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", {"A", "B", "C"})
***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
 ClassificationDiscriminant (X, Y, "ResponseName", {"Y"})
***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
 ClassificationDiscriminant (X, Y, "ResponseName", 1)
***** error<ClassificationDiscriminant: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationDiscriminant (X, Y, "ClassNames", @(x)x)
***** error<ClassificationDiscriminant: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationDiscriminant (X, Y, "ClassNames", ['a'])
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant (X, ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant ([1;2;3;4;5], {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant (X, logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationDiscriminant: 'Prior' must be either a numeric or a character vector.> ...
 ClassificationDiscriminant (X, Y, "Prior", {"1", "2"})
***** error<ClassificationDiscriminant: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
 ClassificationDiscriminant (X, ones (10,1), "Prior", [1 2])
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", [1, 2])
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", "string")
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", {eye(2)})
***** error<ClassificationDiscriminant: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
 ClassificationDiscriminant (X, Y, "Cost", ones (3))
***** error<ClassificationDiscriminant: Predictor 'x1' has zero within-class variance.> ...
 ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2])
***** error<ClassificationDiscriminant: Predictor 'A' has zero within-class variance.> ...
 ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2], "PredictorNames", {"A", "B"})
***** error<ClassificationDiscriminant: Predictor 'x2' has zero within-class variance.> ...
 ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1))
***** error<ClassificationDiscriminant: Predictor 'B' has zero within-class variance.> ...
 ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1), "PredictorNames", {"A", "B"})
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
***** test
 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcdiscr (x, y);
 [label, score, cost] = predict (Mdl, xc);
 l = {'setosa'; 'versicolor'; 'virginica'};
 s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
 c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
 assert (label, l)
 assert (score, s, 1e-4)
 assert (cost, c, 1e-4)
***** error<ClassificationDiscriminant.predict: too few input arguments.> ...
 predict (MODEL)
***** error<ClassificationDiscriminant.predict: XC is empty.> ...
 predict (MODEL, [])
***** error<ClassificationDiscriminant.predict: XC must have the same number of predictors as the trained model.> ...
 predict (MODEL, 1)
***** test
 load fisheriris
 model = fitcdiscr (meas, species);
 x = mean (meas);
 y = {'versicolor'};
 L = loss (model, x, y);
 assert (L, 0)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "Gamma", 0.4);
 x_test = [1, 6; 3, 3];
 y_test = {'A'; 'B'};
 L = loss (model, x_test, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3];
 y_test = ['1'];
 L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
 assert (L, 0.2423, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'hinge');
 assert (L, 0.5886, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
 assert (L, 0.5107, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, x, y, 'LossFun', customLossFun);
 assert (L, 0.8889, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = [1; 2; 1];
 model = fitcdiscr (x, y);
 L = loss (model, x, y, 'LossFun', 'classiferror');
 assert (L, 0.3333, 1e-4)
***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL)
***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL, ones (4,2))
***** error<ClassificationDiscriminant.loss: X is empty.> ...
 loss (MODEL, [], zeros (2))
***** error<ClassificationDiscriminant.loss: X must have the same number of predictors as the trained model.> ...
 loss (MODEL, 1, zeros (2))
***** error<ClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
***** error<ClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
 loss (MODEL, ones (4,2), ones (3,1))
***** error<ClassificationDiscriminant.loss: invalid loss function.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
***** error<ClassificationDiscriminant.loss: invalid 'Weights'.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1, 1e-6)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 mdl = fitcdiscr (X, Y, "gamma", 0.5);
 m = margin (mdl, X, Y);
 assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL)
***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL, ones (4,2))
***** error<ClassificationDiscriminant.margin: X is empty.> ...
 margin (MODEL, [], zeros (2))
***** error<ClassificationDiscriminant.margin: X must have the same number of predictors as the trained model.> ...
 margin (MODEL, 1, zeros (2))
***** error<ClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
 margin (MODEL, ones (4,2), ones (3,1))
***** shared x, y, obj
 load fisheriris
 x = meas;
 y = species;
 obj = fitcdiscr (x, y, "gamma", 0.4);
***** test
 CVMdl = crossval (obj);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 10)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 CVMdl = crossval (obj, "KFold", 3);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 partition = cvpartition (y, 'KFold', 3);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** error<ClassificationDiscriminant.crossval: Name-Value arguments must be in pairs.> ...
 crossval (obj, "kfold")
***** error<ClassificationDiscriminant.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (obj, "kfold", 12, "holdout", 0.2)
***** error<ClassificationDiscriminant.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (obj, "kfold", 'a')
***** error<ClassificationDiscriminant.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (obj, "holdout", 2)
***** error<ClassificationDiscriminant.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (obj, "leaveout", 1)
***** error<ClassificationDiscriminant.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (obj, "cvpartition", 1)
65 tests, 65 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationGAM.m
***** demo
 ## Train a GAM classifier for binary classification
 ## using specific data and plot the decision boundaries.

 ## Define specific data
 X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
     6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
 Y = [0; 0; 0; 0; 0; ...
     1; 1; 1; 1; 1];

 ## Train the GAM model
 obj = fitcgam (X, Y, "Interactions", "all")

 ## Create a grid of values for prediction
 x1 = [min(X(:,1)):0.1:max(X(:,1))];
 x2 = [min(X(:,2)):0.1:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 [labels, score] = predict (obj, XGrid);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 a = ClassificationGAM (x, y, "PredictorNames", PredictorNames);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, PredictorNames)
 assert (a.BaseModel.Intercept, 0)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
 assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (a.ModelwInt.Intercept, 0)
***** test
 X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
 Y = [0; 1; 0; 1; 1];
 a = ClassificationGAM (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, {'x1', 'x2', 'x3'})
 assert (a.Knots, [4, 4, 4])
 assert (a.Order, [3, 3, 3])
 assert (a.DoF, [7, 7, 7])
 assert (a.BaseModel.Intercept, 0.4055, 1e-1)
***** error<ClassificationGAM: too few input arguments.> ClassificationGAM ()
***** error<ClassificationGAM: too few input arguments.> ...
 ClassificationGAM (ones(4, 1))
***** error<ClassificationGAM: number of rows in X and Y must be equal.> ...
 ClassificationGAM (ones (4,2), ones (1,4))
***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationGAM: 'PredictorNames' must equal the number of columns in X.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationGAM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationGAM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", ['a'])
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", [1, 2])
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", "string")
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", {eye(2)})
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 a = ClassificationGAM (x, y, "interactions", "all");
 l = {'0'; '0'; '0'; '0'; '0'};
 s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
      0.4259, 0.5741; 0.3760, 0.6240];
 [labels, scores] = predict (a, x);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
 assert (a.ClassNames, {'1'; '0'})
 assert (a.PredictorNames, {'x1', 'x2'})
 assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
 assert (labels, l)
 assert (scores, s, 1e-1)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 interactions = [false, true, false; true, false, true; false, true, false];
 a = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
 [label, score] = predict (a, x, "includeinteractions", true);
 l = {'0'; '0'; '1'; '1'};
 s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, {'x1', 'x2', 'x3'})
 assert (a.ModelwInt.Intercept, 0)
 assert (label, l)
 assert (score, s, 1e-1)
***** error<ClassificationGAM.predict: too few input arguments.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)))
***** error<ClassificationGAM.predict: XC is empty.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)), [])
***** error<ClassificationGAM.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)), 1)
***** shared x, y, obj
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 obj = fitcgam (x, y);
***** test
 CVMdl = crossval (obj);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 10)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 CVMdl = crossval (obj, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 partition = cvpartition (y, 'KFold', 3);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
 crossval (obj, "kfold")
***** error<ClassificationGAM.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (obj, "kfold", 12, "holdout", 0.2)
***** error<ClassificationGAM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (obj, "kfold", 'a')
***** error<ClassificationGAM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (obj, "holdout", 2)
***** error<ClassificationGAM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (obj, "leaveout", 1)
***** error<ClassificationGAM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (obj, "cvpartition", 1)
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationNeuralNetwork.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationNeuralNetwork.m
***** error<ClassificationNeuralNetwork: too few input arguments.> ...
 ClassificationNeuralNetwork ()
***** error<ClassificationNeuralNetwork: too few input arguments.> ...
 ClassificationNeuralNetwork (ones(10,2))
***** error<ClassificationNeuralNetwork: number of rows in X and Y must be equal.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (5,1))
***** error<ClassificationNeuralNetwork: 'Standardize' must be either true or false.> ...
 ClassificationNeuralNetwork (ones (5,3), ones (5,1), "standardize", "a")
***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationNeuralNetwork: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", ['a'])
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", -1)
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", 0.5)
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [1,-2])
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10,20,30.5])
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", -0.1)
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", [0.1, 0.01])
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", "a")
***** error<ClassificationNeuralNetwork: 'Activations' must be a character vector or a cellstring vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", 123)
***** error<ClassificationNeuralNetwork: unsupported 'Activation' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", "unsupported_type")
***** error<ClassificationNeuralNetwork: unsupported 'Activation' functions.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10, 5], ...
 "Activations", {"sigmoid", "unsupported_type"})
***** error<ClassificationNeuralNetwork: 'Activations' vector does not match the number of layers.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", {"sigmoid", "relu", "softmax"})
***** error<ClassificationNeuralNetwork: 'OutputLayerActivation' must be a character vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", 123)
***** error<ClassificationNeuralNetwork: unsupported 'OutputLayerActivation' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", "unsupported_type")
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", -1)
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", 0.5)
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", [1,2])
***** error<ClassificationNeuralNetwork: 'ScoreTransform' must be a character vector or a function handle.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", [1,2])
***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", "unsupported_type")
***** error<ClassificationNeuralNetwork: invalid parameter name in optional pair arguments.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "some", "some")
***** error<ClassificationNeuralNetwork: invalid values in X.> ...
 ClassificationNeuralNetwork ([1;2;3;'a';4], ones (5,1))
***** error<ClassificationNeuralNetwork: invalid values in X.> ...
 ClassificationNeuralNetwork ([1;2;3;Inf;4], ones (5,1))
***** shared x, y, objST, Mdl
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 100);
***** error<ClassificationNeuralNetwork.predict: too few input arguments.> ...
 predict (Mdl)
***** error<ClassificationNeuralNetwork.predict: XC is empty.> ...
 predict (Mdl, [])
***** error<ClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained model.> ...
 predict (Mdl, 1)
***** test
 objST = fitcnet (x, y, "IterationLimit", 100);
 objST.ScoreTransform = "a";
***** error<ClassificationNeuralNetwork.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (objST, x);
***** error<ClassificationNeuralNetwork.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = resubPredict (objST);
***** test
 CVMdl = crossval (Mdl, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
 assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
***** test
 CVMdl = crossval (Mdl, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
 assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
***** error<ClassificationNeuralNetwork.crossval: Name-Value arguments must be in pairs.> ...
 crossval (Mdl, "KFold")
***** error<ClassificationNeuralNetwork.crossval: specify only one of the optional Name-Value paired arguments.> ...
 crossval (Mdl, "KFold", 5, "leaveout", 'on')
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 'a')
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 1)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", -1)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 11.5)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", [1,2])
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 'a')
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 11.5)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", -1)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 0)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 1)
***** error<ClassificationNeuralNetwork.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (Mdl, "Leaveout", 1)
***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (Mdl, "CVPartition", 1)
***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (Mdl, "CVPartition", 'a')
***** error<ClassificationNeuralNetwork.crossval: invalid parameter name in optional paired arguments> ...
 crossval (Mdl, "some", "some")
59 tests, 59 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/CompactClassificationGAM.m
***** demo
 ## Create a generalized additive model classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl);

 whos ('Mdl', 'CMdl')
***** test
 Mdl = CompactClassificationGAM ();
 assert (class (Mdl), "CompactClassificationGAM")
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 Mdl = fitcgam (x, y, "PredictorNames", PredictorNames);
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, PredictorNames)
 assert (CMdl.BaseModel.Intercept, 0)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 Mdl = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {4, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (CMdl.ModelwInt.Intercept, 0)
***** test
 X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
 Y = [0; 1; 0; 1; 1];
 Mdl = fitcgam (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
 assert (CMdl.Knots, [4, 4, 4])
 assert (CMdl.Order, [3, 3, 3])
 assert (CMdl.DoF, [7, 7, 7])
 assert (CMdl.BaseModel.Intercept, 0.4055, 1e-1)
***** error<CompactClassificationGAM: invalid classification object.> ...
 CompactClassificationGAM (1)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 Mdl = fitcgam (x, y, "interactions", "all");
 CMdl = compact (Mdl);
 l = {'0'; '0'; '0'; '0'; '0'};
 s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
      0.4259, 0.5741; 0.3760, 0.6240];
 [labels, scores] = predict (CMdl, x);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {2, "Y"})
 assert (CMdl.ClassNames, {'1'; '0'})
 assert (CMdl.PredictorNames, {'x1', 'x2'})
 assert (CMdl.ModelwInt.Intercept, 0.4055, 1e-1)
 assert (labels, l)
 assert (scores, s, 1e-1)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 interactions = [false, true, false; true, false, true; false, true, false];
 Mdl = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
 CMdl = compact (Mdl);
 [label, score] = predict (CMdl, x, "includeinteractions", true);
 l = {'0'; '0'; '1'; '1'};
 s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
 assert (CMdl.ModelwInt.Intercept, 0)
 assert (label, l)
 assert (score, s, 1e-1)
***** shared CMdl
 Mdl = fitcgam (ones (4,2), ones (4,1));
 CMdl = compact (Mdl);
***** error<CompactClassificationGAM.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationGAM.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationGAM.predict: XC must have the same number of features as the trained model.> ...
 predict (CMdl, 1)
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationDiscriminant.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/CompactClassificationDiscriminant.m
***** demo
 ## Create a discriminant analysis classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl);

 whos ('Mdl', 'CMdl')
***** test
 load fisheriris
 x = meas;
 y = species;
 PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
 Mdl = fitcdiscr (x, y, "PredictorNames", PredictorNames);
 CMdl = compact (Mdl);
 sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
          0.092721, 0.115388, 0.055244, 0.032710; ...
          0.167514, 0.055244, 0.185188, 0.042665; ...
          0.038401, 0.032710, 0.042665, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (CMdl), "CompactClassificationDiscriminant");
 assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
 assert ({CMdl.Gamma, CMdl.MinGamma}, {0, 0}, 1e-15)
 assert (CMdl.ClassNames, unique (species))
 assert (CMdl.Sigma, sigma, 1e-6)
 assert (CMdl.Mu, mu, 1e-14)
 assert (CMdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (CMdl.LogDetSigma, -9.9585, 1e-4)
 assert (CMdl.PredictorNames, PredictorNames)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (x, y, "Gamma", 0.5);
 CMdl = compact (Mdl);
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (CMdl), "CompactClassificationDiscriminant");
 assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
 assert ({CMdl.Gamma, CMdl.MinGamma}, {0.5, 0})
 assert (CMdl.ClassNames, unique (species))
 assert (CMdl.Sigma, sigma, 1e-6)
 assert (CMdl.Mu, mu, 1e-14)
 assert (CMdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (CMdl.LogDetSigma, -8.6884, 1e-4)
***** error<CompactClassificationDiscriminant: invalid classification object.> ...
 CompactClassificationDiscriminant (1)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 CMdl = compact (Mdl);
 [label, score, cost] = predict (CMdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (CMdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
***** test
 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcdiscr (x, y);
 CMdl = compact (Mdl);
 [label, score, cost] = predict (CMdl, xc);
 l = {'setosa'; 'versicolor'; 'virginica'};
 s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
 c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
 assert (label, l)
 assert (score, s, 1e-4)
 assert (cost, c, 1e-4)
***** shared MODEL
 X = rand (10,2);
 Y = [ones(5,1);2*ones(5,1)];
 MODEL = compact (ClassificationDiscriminant (X, Y));
***** error<CompactClassificationDiscriminant.predict: too few input arguments.> ...
 predict (MODEL)
***** error<CompactClassificationDiscriminant.predict: XC is empty.> ...
 predict (MODEL, [])
***** error<CompactClassificationDiscriminant.predict: XC must have the same number of features as the trained model.> ...
 predict (MODEL, 1)
***** test
 load fisheriris
 model = fitcdiscr (meas, species);
 x = mean (meas);
 y = {'versicolor'};
 L = loss (model, x, y);
 assert (L, 0)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "Gamma", 0.4);
 x_test = [1, 6; 3, 3];
 y_test = {'A'; 'B'};
 L = loss (model, x_test, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3];
 y_test = ['1'];
 L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
 assert (L, 0.2423, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'hinge');
 assert (L, 0.5886, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
 assert (L, 0.5107, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, x, y, 'LossFun', customLossFun);
 assert (L, 0.8889, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = [1; 2; 1];
 model = fitcdiscr (x, y);
 L = loss (model, x, y, 'LossFun', 'classiferror');
 assert (L, 0.3333, 1e-4)
***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL)
***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL, ones (4,2))
***** error<CompactClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
***** error<CompactClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
 loss (MODEL, ones (4,2), ones (3,1))
***** error<CompactClassificationDiscriminant.loss: invalid loss function.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
***** error<CompactClassificationDiscriminant.loss: invalid 'Weights'.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1, 1e-6)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 mdl = fitcdiscr (X, Y, "gamma", 0.5);
 m = margin (mdl, X, Y);
 assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL)
***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL, ones (4,2))
***** error<CompactClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
 margin (MODEL, ones (4,2), ones (3,1))
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationKNN.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationKNN.m
***** demo
 ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
 ## Evaluate some model predictions on new data.

 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
 [label, score, cost] = predict (obj, xc)
***** demo
 load fisheriris
 x = meas;
 y = species;
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create a cross-validated model
 CVMdl = crossval (obj)
***** demo
 load fisheriris
 x = meas;
 y = species;
 covMatrix = cov (x);

 ## Fit the k-NN model using the 'mahalanobis' distance
 ## and the custom covariance matrix
 obj = fitcknn(x, y, 'NumNeighbors', 5, 'Distance','mahalanobis', ...
 'Cov', covMatrix);

 ## Create a partition model using cvpartition
 Partition = cvpartition (size (x, 1), 'kfold', 12);

 ## Create cross-validated model using 'cvPartition' name-value argument
 CVMdl = crossval (obj, 'cvPartition', Partition)

 ## Access the trained model from first fold of cross-validation
 CVMdl.Trained{1}
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 ## Calculate loss using custom loss function
 L = loss (model, X, Y, 'LossFun', customLossFun)
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 ## Calculate loss using 'mincost' loss function
 L = loss (model, X, Y, 'LossFun', 'mincost')
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 ## Specify custom Weights
 W = [1; 2];
 L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
***** demo
 load fisheriris
 mdl = fitcknn (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y)
***** demo
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 ## Train the model
 mdl = fitcknn (X, Y);
 ## Specify Vars and Labels
 Vars = 1;
 Labels = 2;
 ## Calculate partialDependence
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
***** demo
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 ## Train the model
 mdl = fitcknn (X, Y);
 ## Specify Vars and Labels
 Vars = 1;
 Labels = 1;
 queryPoints = [linspace(0, 1, 3)', linspace(0, 1, 3)'];
 ## Calculate partialDependence using queryPoints
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
 queryPoints)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = ones (4, 11);
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k, "Distance", "hamming");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = ClassificationKNN (x, y, "Standardize", 1);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {true})
 assert ({a.Sigma}, {std(x, [], 1)})
 assert ({a.Mu}, {[3.75, 4.25, 4.75]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = ClassificationKNN (x, y, "Standardize", false);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {false})
 assert ({a.Sigma}, {[]})
 assert ({a.Mu}, {[]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 s = ones (1, 3);
 a = ClassificationKNN (x, y, "Scale" , s, "Distance", "seuclidean");
 assert (class (a), "ClassificationKNN");
 assert ({a.DistParameter}, {s})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski", ...
                        "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "BucketSize" , 20, "distance", "mahalanobis");
 assert (class (a), "ClassificationKNN");
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
 assert ({a.BucketSize}, {20})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "IncludeTies", true);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, true);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, false);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, [0.5; 0.5])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 prior = [0.5; 0.5];
 a = ClassificationKNN (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.75; 0.25];
 a = ClassificationKNN (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.5; 0.5];
 a = ClassificationKNN (x, y, "Prior", "uniform");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = ClassificationKNN (x, y, "Cost", cost);
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = ClassificationKNN (x, y, "Cost", cost, "Distance", "hamming" );
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2; 3, 4; 5,6; 5, 8];
 y = {'9'; '9'; '6'; '7'};
 a = ClassificationKNN (x, y);
 assert (a.Prior, [0.5; 0.25; 0.25])
***** test
 load fisheriris
 x = meas;
 y = species;
 ClassNames = {'setosa', 'versicolor', 'virginica'};
 a = ClassificationKNN (x, y, 'ClassNames', ClassNames);
 assert (a.ClassNames, ClassNames')
***** error<ClassificationKNN: too few input arguments.> ClassificationKNN ()
***** error<ClassificationKNN: too few input arguments.> ...
 ClassificationKNN (ones(4, 1))
***** error<ClassificationKNN: number of rows in X and Y must be equal.> ...
 ClassificationKNN (ones (4,2), ones (1,4))
***** error<ClassificationKNN: 'Standardize' must be either true or false.> ...
 ClassificationKNN (ones (5,3), ones (5,1), "standardize", "a")
***** error<ClassificationKNN: 'Standardize' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "standardize", true)
***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationKNN: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationKNN: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationKNN: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", ['a'])
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", 1)
***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", {"1"})
***** error<ClassificationKNN: invalid value for 'BreakTies'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", "some")
***** error<ClassificationKNN: 'Prior' must be either a numeric vector or a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Prior", {"1", "2"})
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1, 2])
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", "string")
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", {eye(2)})
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 0)
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 15.2)
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", "asd")
***** error<ClassificationKNN: unsupported distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", "somemetric")
***** error<ClassificationKNN: invalid function handle for distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
                    @(v,m)sqrt(repmat(v,rows(m),1)-m,2))
***** error<ClassificationKNN: custom distance function produces wrong output size.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
                    @(v,m)sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", [1 2 3])
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", {"mahalanobis"})
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", logical (5))
***** error<ClassificationKNN: function handle for distance weight must return the> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", @(x)sum(x))
***** error<ClassificationKNN: invalid distance weight.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", "text")
***** error<ClassificationKNN: invalid distance weight.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", [1 2 3])
***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", "scale")
***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", {[1 2 3]})
***** error<ClassificationKNN: 'Scale' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "standardize", true, "scale", [1 1])
***** error<ClassificationKNN: 'Cov' must be a symmetric positive definite matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", ones (2), "Distance", "mahalanobis")
***** error<ClassificationKNN: 'Cov' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "Cov", ones (2))
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 12.5)
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", -3)
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", "three")
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", {3})
***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", {"kdtree"})
***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", 3)
***** error<ClassificationKNN: 'NSMethod' must be either 'kdtree' or 'exhaustive'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", "some")
***** error<ClassificationKNN: 'IncludeTies' must be either true or false.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "IncludeTies", "some")
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", 42.5)
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", -50)
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", "some")
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", {50})
***** error<ClassificationKNN: invalid parameter name in optional pair arguments.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "some", "some")
***** error<ClassificationKNN: invalid values in X.> ...
 ClassificationKNN ([1;2;3;'a';4], ones (5,1))
***** error<ClassificationKNN: invalid values in X.> ...
 ClassificationKNN ([1;2;3;Inf;4], ones (5,1))
***** error<ClassificationKNN: the elements in 'Prior' do not correspond to selected classes in Y.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Prior", [1 2])
***** error<ClassificationKNN: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1 2; 1 3])
***** error<ClassificationKNN: 'Scale' is only valid when distance metric is seuclidean.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1])
***** error<ClassificationKNN: 'Scale' vector must have equal length to the number of columns in X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1 1], "Distance", "seuclidean")
***** error<ClassificationKNN: 'Scale' vector must contain nonnegative scalar values.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 -1], "Distance", "seuclidean")
***** error<ClassificationKNN: 'Cov' is only valid when distance metric is 'mahalanobis'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (2))
***** error<ClassificationKNN: 'Cov' matrix must have equal columns as X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (3), "Distance", "mahalanobis")
***** error<ClassificationKNN: 'Exponent' is only valid when distance metric is 'minkowski'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 3)
***** error<ClassificationKNN: 'kdtree' method is only valid for 'euclidean', 'cityblock', 'manhattan', 'minkowski', and 'chebychev' distance metrics.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", "hamming", "NSMethod", "kdtree")
***** shared x, y
 load fisheriris
 x = meas;
 y = species;
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
 assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"; "versicolor"; "virginica"})
 assert (s, [0.4, 0.6, 0; 0, 1, 0; 0, 0, 1])
 assert (c, [0.6, 0.4, 1; 1, 0, 1; 1, 1, 0])
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
 [l, s, c] = predict (obj, xc);
 assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
 assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
 assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
***** test
 xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
         5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
 assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
 assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.6, 0.4], 1e-4)
 assert (c, [1, 0.4, 0.6], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "minkowski", "Exponent", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.5, 0.5], 1e-4)
 assert (c, [1, 0.5, 0.5], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "jaccard");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.9, 0.1, 0], 1e-4)
 assert (c, [0.1, 0.9, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
 assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "jaccard");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.8, 0.2, 0], 1e-4)
 assert (c, [0.2, 0.8, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "seuclidean");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 1, 0], 1e-4)
 assert (c, [1, 0, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "chebychev");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.7, 0.3], 1e-4)
 assert (c, [1, 0.3, 0.7], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cityblock");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.6, 0.4], 1e-4)
 assert (c, [1, 0.4, 0.6], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
 [l, s, c] = predict (obj, xc);
 assert (l, {"virginica"})
 assert (s, [0, 0.1, 0.9], 1e-4)
 assert (c, [1, 0.9, 0.1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
 [l, s, c] = predict (obj, xc);
 assert (l, {"virginica"})
 assert (s, [0, 0.1, 0.9], 1e-4)
 assert (c, [1, 0.9, 0.1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "spearman");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 1, 0], 1e-4)
 assert (c, [1, 0, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
 assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.8, 0.2, 0], 1e-4)
 assert (c, [0.2, 0.8, 1], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
 assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa";"setosa";"setosa"})
 assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
 assert (c, [0.1, 0.9, 1; 0, 1, 1; 0.5, 1, 0.5], 1e-4)
***** error<ClassificationKNN.predict: too few input arguments.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.predict: XC is empty.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)), [])
***** error<ClassificationKNN.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)), 1)
***** test
 load fisheriris
 model = fitcknn (meas, species, 'NumNeighbors', 5);
 X = mean (meas);
 Y = {'versicolor'};
 L = loss (model, X, Y);
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_test = [1, 6; 3, 3];
 Y_test = {'A'; 'B'};
 L = loss (model, X_test, Y_test);
 assert (abs (L - 0.6667) > 1e-5)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_with_nan = [1, 2; NaN, 4];
 Y_test = {'A'; 'B'};
 L = loss (model, X_with_nan, Y_test);
 assert (abs (L - 0.3333) < 1e-4)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_with_nan = [1, 2; NaN, 4];
 Y_test = {'A'; 'B'};
 L = loss (model, X_with_nan, Y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, X, Y, 'LossFun', customLossFun);
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'classiferror');
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [true; false; true];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'binodeviance');
 assert (abs (L - 0.1269) < 1e-4)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '1'];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'classiferror');
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3];
 Y_test = ['1'];
 L = loss (model, X_test, Y_test, 'LossFun', 'quadratic');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 L = loss (model, X_test, Y_test, 'LossFun', 'classifcost');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 L = loss (model, X_test, Y_test, 'LossFun', 'hinge');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
 assert (abs (L - 0.6931) < 1e-4)
***** error<ClassificationKNN.loss: too few input arguments.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.loss: too few input arguments.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
***** error<ClassificationKNN.loss: X is empty.> ...
 loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationKNN.loss: X must have the same number of predictors as the trained model.> ...
 loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationKNN.loss: name-value arguments must be in pairs.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'LossFun')
***** error<ClassificationKNN.loss: Y must have the same number of rows as X.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
***** error<ClassificationKNN.loss: invalid loss function.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'LossFun', 'a')
***** error<ClassificationKNN.loss: invalid Weights.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'Weights', 'w')
***** test
 load fisheriris
 mdl = fitcknn (meas, species, 'NumNeighbors', 5);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 3];
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (m, [1; 1; 1])
***** test
 X = [7, 8; 9, 10];
 Y = ['1'; '2'];
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (m, [1; 1])
***** test
 X = [11, 12];
 Y = {'1'};
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (isnan (m))
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 3];
 mdl = fitcknn (X, Y);
 X1 = [15, 16];
 Y1 = [1];
 m = margin (mdl, X1, Y1);
 assert (m, -1)
***** error<ClassificationKNN.margin: too few input arguments.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.margin: too few input arguments.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
***** error<ClassificationKNN.margin: X is empty.> ...
 margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationKNN.margin: X must have the same number of predictors as the trained model.> ...
 margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationKNN.margin: Y must have the same number of rows as X.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
***** shared X, Y, mdl
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 mdl = fitcknn (X, Y);
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, ...
 'NumObservationsToSample', 5);
 pdm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
 assert (abs (pdm - pd) < 1)
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'UseParallel', true);
 pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = [1, 2];
 Labels = 1;
 queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
                            queryPoints, 'UseParallel', true);
 pdm = [0, 0, 0; 0, 0, 0; 0, 0, 0];
 assert (pd, pdm)
***** test
 Vars = 1;
 Labels = [1; 2];
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [0.2500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500; 0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = [1, 2];
 Labels = [1; 2];
 queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', queryPoints);
 pdm(:,:,1) = [0, 0, 0; 1, 1, 1];
 pdm(:,:,2) = [0, 0, 0; 1, 1, 1];
 pdm(:,:,3) = [0, 0, 0; 1, 1, 1];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 mdl = fitcknn (X, Y, 'ClassNames', {'1', '2', '3'});
 Vars = 1;
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 predictorNames = {'Feature1', 'Feature2'};
 mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
 Vars = 'Feature1';
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 predictorNames = {'Feature1', 'Feature2'};
 mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
 new_X1 = [10; 5; 6; 8; 9; 20; 35; 6];
 new_X2 = ['2'; '2'; '1'; '2'; '1'; '3'; '3'; '2'];
 new_X = [new_X1, double(new_X2)];
 Vars = 'Feature1';
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, new_X);
 pdm = [0, 0, 0, 0, 0, 0.2500, 0.2500, 0.2500, 0.2500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500, 0.7500, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
 1.0000, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 assert (pd, pdm)
***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1)
***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
          ones (4,1), 'NumObservationsToSample')
***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
          ones (4,1), 2)
***** shared x, y, obj
 load fisheriris
 x = meas;
 y = species;
 covMatrix = cov (x);
 obj = fitcknn (x, y, 'NumNeighbors', 5, 'Distance', ...
      'mahalanobis', 'Cov', covMatrix);
***** test
 CVMdl = crossval (obj);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 10)
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (!CVMdl.ModelParameters.Standardize)
***** test
 CVMdl = crossval (obj, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 obj = fitcknn (x, y, "NumNeighbors", 5, "Distance", "cityblock");
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.ModelParameters.NumNeighbors == 10)
 assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
 partition = cvpartition (y, 'KFold', 3);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (CVMdl.ModelParameters.NumNeighbors == 10)
 assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** error<ClassificationKNN.crossval: Name-Value arguments must be in pairs.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold")
***** error<ClassificationKNN.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 12, "holdout", 0.2)
***** error<ClassificationKNN.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 'a')
***** error<ClassificationKNN.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "holdout", 2)
***** error<ClassificationKNN.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "leaveout", 1)
***** error<ClassificationKNN.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "cvpartition", 1)
162 tests, 162 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationSVM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationSVM.m
***** demo
 ## Create a Support Vector Machine classifier and determine margin for test
 ## data.
 load fisheriris
 rng(1);  ## For reproducibility

 ## Select indices of the non-setosa species
 inds = !strcmp(species, 'setosa');

  ## Select features and labels for non-setosa species
 X = meas(inds, 3:4);
 Y = grp2idx(species(inds));

 ##  Convert labels to +1 and -1
 unique_classes = unique(Y);
 Y(Y == unique_classes(1)) = -1;
 Y(Y == unique_classes(2)) = 1;

 ## Partition data for training and testing
 cv = cvpartition(Y, 'HoldOut', 0.15);
 X_train = X(training(cv), :);
 Y_train = Y(training(cv));
 X_test = X(test(cv), :);
 Y_test = Y(test(cv));

 ## Train the SVM model
 CVSVMModel = fitcsvm(X_train, Y_train);

 ## Calculate margins
 m = margin(CVSVMModel, X_test, Y_test);
 disp(m);
***** demo
 ## Create a Support Vector Machine classifier and determine loss for test
 ## data.
 load fisheriris
 rng(1);  ## For reproducibility

  ## Select indices of the non-setosa species
 inds = !strcmp(species, 'setosa');

  ## Select features and labels for non-setosa species
 X = meas(inds, 3:4);
 Y = grp2idx(species(inds));

 ##  Convert labels to +1 and -1
 unique_classes = unique(Y);
 Y(Y == unique_classes(1)) = -1;
 Y(Y == unique_classes(2)) = 1;

 ## Randomly partition the data into training and testing sets
 cv = cvpartition(Y, 'HoldOut', 0.3); # 30% data for testing, 60% for training

 X_train = X(training(cv), :);
 Y_train = Y(training(cv));

 X_test = X(test(cv), :);
 Y_test = Y(test(cv));

 ## Train the SVM model
 SVMModel = fitcsvm(X_train, Y_train);

 ## Calculate loss

 L = loss(SVMModel,X_test,Y_test,'LossFun','binodeviance')
 L = loss(SVMModel,X_test,Y_test,'LossFun','classiferror')
 L = loss(SVMModel,X_test,Y_test,'LossFun','exponential')
 L = loss(SVMModel,X_test,Y_test,'LossFun','hinge')
 L = loss(SVMModel,X_test,Y_test,'LossFun','logit')
 L = loss(SVMModel,X_test,Y_test,'LossFun','quadratic')
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; ...
 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4; 2; 3; 4; 2; 3; 4; 2; 3; 4];
 a = ClassificationSVM (x, y, "ClassNames", [1, 2]);
 assert (class (a), "ClassificationSVM");
 assert (a.RowsUsed, [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]');
 assert ({a.X, a.Y}, {x, y})
 assert (a.NumObservations, 5)
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.ClassNames, a.ModelParameters.SVMtype}, {[1; 2], "c_svc"})
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.BoxConstraint, 1)
 assert (a.ClassNames, [1; -1])
 assert (a.ModelParameters.KernelOffset, 0)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
 "KernelOffset", 2);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
 assert (a.ModelParameters.BoxConstraint, 2)
 assert (a.ModelParameters.KernelOffset, 2)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y, "KernelFunction", "polynomial", ...
 "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
 assert (a.ModelParameters.PolynomialOrder, 3)
***** error<ClassificationSVM: too few input arguments.> ClassificationSVM ()
***** error<ClassificationSVM: too few input arguments.> ...
 ClassificationSVM (ones(10,2))
***** error<ClassificationSVM: number of rows in X and Y must be equal.> ...
 ClassificationSVM (ones(10,2), ones (5,1))
***** error<ClassificationSVM: 'Standardize' must be either true or false.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "Standardize", 'a')
***** error<ClassificationSVM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", ['x1';'x2'])
***** error<ClassificationSVM: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", {'x1','x2','x3'})
***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", {'Y'})
***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", 21)
***** error<ClassificationSVM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationSVM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", ['a'])
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Prior", {"asd"})
***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Prior", ones (2))
***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Cost", [1:4])
***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Cost", {0,1;1,0})
***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Cost", 'a')
***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 123)
***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 'some_type')
***** error<ClassificationSVM: 'OutlierFraction' must be a positive scalar in the range 0 =< OutlierFraction < 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "OutlierFraction", -1)
***** error<ClassificationSVM: 'KernelFunction' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", 123)
***** error<ClassificationSVM: unsupported Kernel function.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", "fcn")
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", -1)
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", 0.5)
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", [1,2])
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", -1)
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", 0)
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", [1, 2])
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", "invalid")
***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", -1)
***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", [1,2])
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", -1)
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", 0)
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", [1, 2])
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", "invalid")
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", -0.5)
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", 0)
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", 1.5)
***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", -1)
***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", [1,2])
***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", -0.1)
***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", [0.1,0.2])
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", 2)
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", -1)
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", [1 0])
***** error<ClassificationSVM: invalid parameter name in optional pair arguments.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "invalid_name", 'c_svc')
***** error<ClassificationSVM: cannot train a binary problem with only one class available.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "SVMtype", 'c_svc')
***** error<ClassificationSVM: can only be used for one-class or two-class learning.> ...
 ClassificationSVM (ones(10,2), [1;1;1;1;2;2;2;2;3;3])
***** error<ClassificationSVM: invalid values in X.> ...
 ClassificationSVM ([ones(9,2);2,Inf], ones(10,1))
***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
 ClassificationSVM (ones (5,2), ones (5,1), "Prior", [0,1])
***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
 ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Prior", [0,0.4,0.6])
***** error<ClassificationSVM: the number of rows and columns in 'Cost' must correspond to the selected classes in Y.> ...
 ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Cost", ones (3))
***** shared x, y, x_train, x_test, y_train, y_test, objST
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 assert (isempty (obj.Alpha), true)
 assert (sum (obj.IsSupportVector), numel (obj.Beta))
 [label, score] = predict (obj, xc);
 assert (label, [1; 2; 2]);
 assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
 assert (score(:,1), -score(:,2), eps)
 obj = fitPosterior (obj);
 [label, probs] = predict (obj, xc);
 assert (probs(:,2), [0.97555; 0.428164; 0.030385], 1e-5);
 assert (probs(:,1) + probs(:,2), [1; 1; 1], 0.05)
***** test
 obj = fitcsvm (x, y);
 assert (isempty (obj.Beta), true)
 assert (sum (obj.IsSupportVector), numel (obj.Alpha))
 assert (numel (obj.Alpha), 24)
 assert (obj.Bias, -14.415, 1e-3)
 xc = [min(x); mean(x); max(x)];
 label = predict (obj, xc);
 assert (label, [1; 2; 2]);
***** error<ClassificationSVM.predict: too few input arguments.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)))
***** error<ClassificationSVM.predict: XC is empty.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)), [])
***** error<ClassificationSVM.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)), 1)
***** test
 objST = fitcsvm (x, y);
 objST.ScoreTransform = "a";
***** error<ClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (objST, x);
***** error<ClassificationSVM.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = resubPredict (objST);
***** test
 rand ("seed", 1);
 CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15, ...
                       'Tolerance', 1e-7);
 obj = CVSVMModel.Trained{1};
 testInds = test (CVSVMModel.Partition);
 expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
                    2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
                   -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
 computed_margin = margin (obj, x(testInds,:), y(testInds,:));
 assert (computed_margin, expected_margin, 1e-4);
***** error<ClassificationSVM.margin: too few input arguments.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
***** error<ClassificationSVM.margin: too few input arguments.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
***** error<ClassificationSVM.margin: X is empty.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationSVM.margin: X must have the same number of predictors as the trained model.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationSVM.margin: Y is empty.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
***** error<ClassificationSVM.margin: Y must have the same number of rows as X.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
***** test
 rand ("seed", 1);
 CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15);
 obj = CVSVMModel.Trained{1};
 testInds = test (CVSVMModel.Partition);
 L1 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
 L2 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
 L3 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
 L4 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
 L5 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
 L6 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
 assert (L1, 2.8711, 1e-4);
 assert (L2, 0.5333, 1e-4);
 assert (L3, 10.9685, 1e-4);
 assert (L4, 1.9827, 1e-4);
 assert (L5, 1.5849, 1e-4);
 assert (L6, 7.6739, 1e-4);
***** error<ClassificationSVM.loss: too few input arguments.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
***** error<ClassificationSVM.loss: too few input arguments.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
***** error<ClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones(2,1), "LossFun")
***** error<ClassificationSVM.loss: X is empty.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationSVM.loss: X must have the same number of predictors as the trained model.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationSVM.loss: Y is empty.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
***** error<ClassificationSVM.loss: Y must have the same number of rows as X.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
***** error<ClassificationSVM.loss: 'LossFun' must be a character vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "LossFun", 1)
***** error<ClassificationSVM.loss: unsupported Loss function.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "LossFun", "some")
***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", ['a','b'])
***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", 'a')
***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", [1,2,3])
***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", 3)
***** error<ClassificationSVM.loss: invalid parameter name in optional pair arg> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "some", "some")
***** error<ClassificationSVM.resubLoss: Name-Value arguments must be in pairs.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun")
***** error<ClassificationSVM.resubLoss: 'LossFun' must be a character vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", 1)
***** error<ClassificationSVM.resubLoss: unsupported Loss function.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", "some")
***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", ['a','b'])
***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 'a')
***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", [1,2,3])
***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 3)
***** error<ClassificationSVM.resubLoss: invalid parameter name in optional pai> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "some", "some")
***** test
 SVMModel = fitcsvm (x, y);
 CVMdl = crossval (SVMModel, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** test
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** test
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** error<ClassificationSVM.crossval: Name-Value arguments must be in pairs.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold")
***** error<ClassificationSVM.crossval: specify only one of the optional Name-Value paired arguments.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), ...
 "KFold", 5, "leaveout", 'on')
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 'a')
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 1)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", -1)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 11.5)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", [1,2])
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 'a')
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 11.5)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", -1)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 0)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 1)
***** error<ClassificationSVM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Leaveout", 1)
***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 1)
***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 'a')
***** error<ClassificationSVM.crossval: invalid parameter name in optional paired arguments> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "some", "some")
114 tests, 114 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationNeuralNetwork.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/CompactClassificationNeuralNetwork.m
***** demo
 ## Create a neural network classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl);

 whos ('Mdl', 'CMdl')
***** error<CompactClassificationDiscriminant: invalid classification object.> ...
 CompactClassificationDiscriminant (1)
***** shared x, y, CMdl
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 100);
 CMdl = compact (Mdl);
***** error<CompactClassificationNeuralNetwork.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationNeuralNetwork.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained neural network.> ...
 predict (CMdl, 1)
***** test
 CMdl.ScoreTransform = "a";
***** error<CompactClassificationNeuralNetwork.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (CMdl, x);
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/vartestn.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/vartestn.m
***** demo
 ## Test the null hypothesis that the variances are equal across the five
 ## columns of data in the students’ exam grades matrix, grades.

 load examgrades
 vartestn (grades)
***** demo
 ## Test the null hypothesis that the variances in miles per gallon (MPG) are
 ## equal across different model years.

 load carsmall
 vartestn (MPG, Model_Year)
***** demo
 ## Use Levene’s test to test the null hypothesis that the variances in miles
 ## per gallon (MPG) are equal across different model years.

 load carsmall
 p = vartestn (MPG, Model_Year, "TestType", "LeveneAbsolute")
***** demo
 ## Test the null hypothesis that the variances are equal across the five
 ## columns of data in the students’ exam grades matrix, grades, using the
 ## Brown-Forsythe test.  Suppress the display of the summary table of
 ## statistics and the box plot.

 load examgrades
 [p, stats] = vartestn (grades, "TestType", "BrownForsythe", "Display", "off")
***** error<vartestn: too few input arguments.> vartestn ();
***** error<vartestn: X must be a vector or a matrix.> vartestn (1);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7]);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], []);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], "TestType", "LeveneAbsolute");
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], [], "TestType", "LeveneAbsolute");
***** error<vartestn: invalid value for display.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], [1, 1, 1, 2, 2, 2, 2], "Display", "some");
***** error<vartestn: invalid value for display.> ...
 vartestn (ones (50,3), "Display", "some");
***** error<vartestn: invalid value for testtype.> ...
 vartestn (ones (50,3), "Display", "off", "testtype", "some");
***** error<vartestn: optional arguments must be in name/value pairs.> ...
 vartestn (ones (50,3), [], "som");
***** error<vartestn: invalid name for optional arguments.> ...
 vartestn (ones (50,3), [], "some", "some");
***** error<vartestn: columns in X and GROUP length do not match.> ...
 vartestn (ones (50,3), [1, 2], "Display", "off");
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off");
 assert (p, 7.908647337018238e-08, 1e-14);
 assert (stat.chisqstat, 38.7332, 1e-4);
 assert (stat.df, 4);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneAbsolute");
 assert (p, 9.523239714592791e-07, 1e-14);
 assert (stat.fstat, 8.5953, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneQuadratic");
 assert (p, 7.219514351897161e-07, 1e-14);
 assert (stat.fstat, 8.7503, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "BrownForsythe");
 assert (p, 1.312093241723211e-06, 1e-14);
 assert (stat.fstat, 8.4160, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "OBrien");
 assert (p, 8.235660885480556e-07, 1e-14);
 assert (stat.fstat, 8.6766, 1e-4);
 assert (stat.df, [4, 595]);
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/confusionchart.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/confusionchart.m
***** demo
 ## Setting the chart properties
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 confusionchart (Yt, Yp, "Title", ...
   "Demonstration with summaries","Normalization",...
   "absolute","ColumnSummary", "column-normalized","RowSummary",...
   "row-normalized")
***** demo
 ## Cellstr as inputs
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 m = confusionmat (Yt, Yp);
 confusionchart (m, {"Positive", "Negative"});
***** demo
 ## Editing the object properties
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 cm = confusionchart (Yt, Yp);
 cm.Title = "This is an example with a green diagonal";
 cm.DiagonalColor = [0.4660, 0.6740, 0.1880];
***** demo
 ## Confusion chart in a uipanel
 h = uipanel ();
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 cm = confusionchart (h, Yt, Yp);
***** demo
 ## Sorting classes
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 cm = confusionchart (Yt, Yp, "Title", ...
   "Classes are sorted in ascending order");
 cm = confusionchart (Yt, Yp, "Title", ...
   "Classes are sorted according to clusters");
 sortClasses (cm, "cluster");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ()", "Invalid call");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 1; 2 2; 3 3])", "invalid argument");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'xxx', 1)", "invalid property");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'XLabel', 1)", "XLabel .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'YLabel', [1 0])", ...
       ".* YLabel .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Title', .5)", ".* Title .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'FontName', [])", ...
       ".* FontName .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'FontSize', 'b')", ...
       ".* FontSize .* numeric");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'DiagonalColor', 'h')", ...
       ".* DiagonalColor .* color");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'OffDiagonalColor', [])", ...
       ".* OffDiagonalColor .* color");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Normalization', '')", ...
       ".* invalid .* Normalization");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'ColumnSummary', [])", ...
       ".* invalid .* ColumnSummary");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'RowSummary', 1)", ...
       ".* invalid .* RowSummary");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'GridVisible', .1)", ...
       ".* invalid .* GridVisible");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'HandleVisibility', .1)", ...
       ".* invalid .* HandleVisibility");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'OuterPosition', .1)", ...
       ".* invalid .* OuterPosition");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Position', .1)", ...
       ".* invalid .* Position");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Units', .1)", ".* invalid .* Units");
 set (0, "DefaultFigureVisible", visibility_setting);
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/clusterdata.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/clusterdata.m
***** demo
 randn ("seed", 1)  # for reproducibility
 r1 = randn (10, 2) * 0.25 + 1;
 randn ("seed", 5)  # for reproducibility
 r2 = randn (20, 2) * 0.5 - 1;
 X = [r1; r2];

 wnl = warning ("off", "Octave:linkage_savemem", "local");
 T = clusterdata (X, "linkage", "ward", "MaxClust", 2);
 scatter (X(:,1), X(:,2), 36, T, "filled");
***** error<clusterdata: function called with too few input arguments.> ...
 clusterdata ()
***** error<clusterdata: function called with too few input arguments.> ...
 clusterdata (1)
***** error <unknown property .*> clusterdata ([1 1], "Bogus", 1)
***** error <specify .* 'MaxClust' or 'Cutoff' .*> clusterdata ([1 1], "Depth", 1)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/knnsearch.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/knnsearch.m
***** demo
 ## find 10 nearest neighbour of a point using different distance metrics
 ## and compare the results by plotting
 load fisheriris
 X = meas(:,3:4);
 Y = species;
 point = [5, 1.45];

 ## calculate 10 nearest-neighbours by minkowski distance
 [id, d] = knnsearch (X, point, "K", 10);

 ## calculate 10 nearest-neighbours by minkowski distance
 [idm, dm] = knnsearch (X, point, "K", 10, "distance", "minkowski", "p", 5);

 ## calculate 10 nearest-neighbours by chebychev distance
 [idc, dc] = knnsearch (X, point, "K", 10, "distance", "chebychev");

 ## plotting the results
 gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
 title ("Fisher's Iris Data - Nearest Neighbors with different types of distance metrics");
 xlabel("Petal length (cm)");
 ylabel("Petal width (cm)");

 line (point(1), point(2), "marker", "X", "color", "k", ...
       "linewidth", 2, "displayname", "query point")
 line (X(id,1), X(id,2), "color", [0.5 0.5 0.5], "marker", "o", ...
       "linestyle", "none", "markersize", 10, "displayname", "eulcidean")
 line (X(idm,1), X(idm,2), "color", [0.5 0.5 0.5], "marker", "d", ...
       "linestyle", "none", "markersize", 10, "displayname", "Minkowski")
 line (X(idc,1), X(idc,2), "color", [0.5 0.5 0.5], "marker", "p", ...
       "linestyle", "none", "markersize", 10, "displayname", "chebychev")
 xlim ([4.5 5.5]);
 ylim ([1 2]);
 axis square;
***** demo
 ## knnsearch on iris dataset using kdtree method
 load fisheriris
 X = meas(:,3:4);
 gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
 title ("Fisher's iris dataset : Nearest Neighbors with kdtree search");

 ## new point to be predicted
 point = [5 1.45];

 line (point(1), point(2), "marker", "X", "color", "k", ...
       "linewidth", 2, "displayname", "query point")

 ## knnsearch using kdtree method
 [idx, d] = knnsearch (X, point, "K", 10, "NSMethod", "kdtree");

 ## plotting predicted neighbours
 line (X(idx,1), X(idx,2), "color", [0.5 0.5 0.5], "marker", "o", ...
       "linestyle", "none", "markersize", 10, ...
       "displayname", "nearest neighbour")
 xlim ([4 6])
 ylim ([1 3])
 axis square
 ## details of predicted labels
 tabulate (species(idx))

 ctr = point - d(end);
 diameter = 2 * d(end);
 ##  Draw a circle around the 10 nearest neighbors.
 h = rectangle ("position", [ctr, diameter, diameter], "curvature", [1 1]);

 ## here only 8 neighbours are plotted instead of 10 since the dataset
 ## contains duplicate values
***** shared X, Y
 X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 Y = [1, 2, 2, 3; 2, 3, 3, 4];
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = knnsearch (X, Y, "Distance", eucldist);
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (idx {1}, [1]);
 assert (idx {2}, [1, 2]);
 assert (D{1}, ones (1, 1) * sqrt (2));
 assert (D{2}, ones (1, 2) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "seuclidean");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "seuclidean", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
***** test
 xx = [1, 2; 1, 3; 2, 4; 3, 6];
 yy = [2, 4; 2, 6];
 [idx, D] = knnsearch (xx, yy, "Distance", "mahalanobis");
 assert (idx, [3; 2]);
 assert (D, [0; 3.162277660168377], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "minkowski");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "minkowski", "p", 3);
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 1.259921049894873, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "cityblock");
 assert (idx, [1; 1]);
 assert (D, [2; 2]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "chebychev");
 assert (idx, [1; 1]);
 assert (D, [1; 1]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "cosine");
 assert (idx, [2; 3]);
 assert (D, [0.005674536395645; 0.002911214328620], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "correlation");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "spearman");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "hamming");
 assert (idx, [1; 1]);
 assert (D, [0.5; 0.5]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "jaccard");
 assert (idx, [1; 1]);
 assert (D, [0.5; 0.5]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "jaccard", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [0.5, 1; 0.5, 0.5]);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
 assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000],1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
 assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000],1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", false);
 assert (iscell (idx), false);
 assert (iscell (D), false)
 assert (idx, [4, 2, 3, 6, 1]);
 assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000],1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", false);
 assert (iscell (idx), false);
 assert (iscell (D), false)
 assert (idx, [4, 2, 3, 6, 1]);
 assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000],1e-4);
***** test
 load fisheriris
 a = meas;
 b = min(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [42, 9, 14, 39, 13]);
 assert (D, [0.5099, 0.9950, 1.0050, 1.0536, 1.1874],1e-4);
***** test
 load fisheriris
 a = meas;
 b = mean(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [65, 83, 89, 72, 100]);
 assert (D, [0.3451, 0.3869, 0.4354, 0.4481, 0.4625],1e-4);
***** test
 load fisheriris
 a = meas;
 b = max(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [118, 132, 110, 106, 136]);
 assert (D, [0.7280, 0.9274, 1.3304, 1.5166, 1.6371],1e-4);

***** test
 load fisheriris
 a = meas;
 b = max(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "includeties", true);
 assert ( iscell (idx), true);
 assert ( iscell (D), true);
 assert (cell2mat (idx), [118, 132, 110, 106, 136]);
 assert (cell2mat (D), [0.7280, 0.9274, 1.3304, 1.5166, 1.6371],1e-4);
***** error<knnsearch: too few input arguments.> knnsearch (1)
***** error<knnsearch: number of columns in X and Y must match.> ...
 knnsearch (ones (4, 5), ones (4))
***** error<knnsearch: invalid NAME in optional pairs of arguments.> ...
 knnsearch (ones (4, 2), ones (3, 2), "Distance", "euclidean", "some", "some")
***** error<knnsearch: only a single distance parameter can be defined.> ...
 knnsearch (ones (4, 5), ones (1, 5), "scale", ones (1, 5), "P", 3)
***** error<knnsearch: invalid value of K.> ...
 knnsearch (ones (4, 5), ones (1, 5), "K", 0)
***** error<knnsearch: invalid value of Minkowski Exponent.> ...
 knnsearch (ones (4, 5), ones (1, 5), "P",-2)
***** error<knnsearch: invalid value in Scale or the size of Scale.> ...
 knnsearch (ones (4, 5), ones (1, 5), "scale", ones(4,5), "distance", "euclidean")
***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 knnsearch (ones (4, 5), ones (1, 5), "cov", ["some" "some"])
***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 knnsearch (ones (4, 5), ones (1, 5), "cov", ones(4,5), "distance", "euclidean")
***** error<knnsearch: invalid value of bucketsize.> ...
 knnsearch (ones (4, 5), ones (1, 5), "bucketsize", -1)
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "cosine")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "mahalanobis")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "correlation")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "seuclidean")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "spearman")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "hamming")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "jaccard")
42 tests, 42 passed, 0 known failure, 0 skipped
[inst/squareform.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/squareform.m
***** shared v, m
 v = 1:6;
 m = [0 1 2 3;1 0 4 5;2 4 0 6;3 5 6 0];
***** assert (squareform (v), m)
***** assert (squareform (squareform (v)), v)
***** assert (squareform (m), v)
***** assert (squareform (v'), m)
***** assert (squareform (1), [0 1;1 0])
***** assert (squareform (1, "tomatrix"), [0 1; 1 0])
***** assert (squareform (0, "tovector"), zeros (1, 0))
***** warning <not a symmetric matrix> squareform ([0 1 2; 3 0 4; 5 6 0]);
***** test
 for c = {@single, @double, @uint8, @uint32, @uint64}
   f = c{1};
   assert (squareform (f (v)), f (m))
   assert (squareform (f (m)), f (v))
 endfor
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/fillmissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fillmissing.m
***** assert (fillmissing ([1, 2, 3], "constant", 99), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 99), [1, 2, 99])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 99), [99, 2, 99])
***** assert (fillmissing ([1, 2, 3]', "constant", 99), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN]', "constant", 99), [1, 2, 99]')
***** assert (fillmissing ([1, 2, 3; 4, 5, 6], "constant", 99), [1, 2, 3; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "constant", 99), [1, 2, 99; 4, 99, 6])
***** assert (fillmissing ([NaN, 2, NaN; 4, NaN, 6], "constant", [97, 98, 99]), [97, 2, 99; 4, 98, 6])
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, 6], [NaN, 2, 3; 4, 5, NaN]);
 y = cat (3, [1, 2, 99; 4, 99, 6], [99, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", 99), y);
 y = cat (3, [1, 2, 96; 4, 95, 6], [97, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [94:99]), y);
 assert (fillmissing (x, "constant", [94:99]'), y);
 assert (fillmissing (x, "constant", permute ([94:99], [1 3 2])), y);
 assert (fillmissing (x, "constant", [94, 96, 98; 95, 97, 99]), y);
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y = cat (3, [1, 2, 96; 4, 97, 6], [98, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [96:99], 2), y);
 y = cat (3, [1, 2, 98; 4, 97, 6], [94, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [94:99], 3), y);
 y = cat (3, [1, 2, 92; 4, 91, 6], [94, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [88:99], 99), y);
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
 assert (fillmissing (x, "constant", [92:99], 2), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [88:99], 3), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [76:99], 99), y);
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", 88), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", 88), [1, 99, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 99, "endvalues", 88), [1, 2, 88])
***** assert (fillmissing ([NaN, 2, 3], "constant", 99, "endvalues", 88), [88, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 99, "endvalues", 88), [88, 88, 3])
***** assert (fillmissing ([1, NaN, NaN], "constant", 99, "endvalues", 88), [1, 88, 88])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 99, "endvalues", 88), [88, 2, 88])
***** assert (fillmissing ([NaN, 2, NaN]', "constant", 99, "endvalues", 88), [88, 2, 88]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 3, 99, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 99, 99, 5])
***** assert (fillmissing ([NaN, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [88, 88, 88, 88, 5])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, "endvalues", 88), [1, 99, 3, 4, 88])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", 88), [1, 88, 3, 4, 88])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", "extrap"), [1, 99, 3, 4, 99])
***** test
 x = reshape ([1:24], 3, 4, 2);
 y = x;
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 88;
 y([8]) = 99;
 assert (fillmissing (x, "constant", 99, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 1, "endvalues", 88), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 88;
 y([6, 18, 20, 21]) = 99;
 assert (fillmissing (x, "constant", 99, 2, "endvalues", 88), y);
 y(y == 99) = 88;
 assert (fillmissing (x, "constant", 99, 3, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 4, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 99, "endvalues", 88), y);
 y([8]) = 94;
 assert (fillmissing (x, "constant", [92:99], 1, "endvalues", 88), y);
 y([6, 8, 18, 20, 21]) = [96, 88, 99, 98, 99];
 assert (fillmissing (x, "constant", [94:99], 2, "endvalues", 88), y);
 y = x;
 y(isnan (y)) = 88;
 assert (fillmissing (x, "constant", [88:99], 3, "endvalues", 88), y);
 y = x;
 y(isnan (y)) = [82, 82, 83, 83, 94, 85, 86, 87, 87, 88, 88, 88, 89];
 assert (fillmissing (x, "constant", [92:99], 1, "endvalues", [82:89]), y);
 y = x;
 y(isnan (y)) = [84, 85, 85, 96, 85, 84, 87, 87, 99, 87, 98, 99, 87];
 assert (fillmissing (x, "constant", [94:99], 2, "endvalues", [84:89]), y);
 y = x;
 y(isnan (y)) = [68, 69, 72, 73, 75, 77, 68, 71, 73, 74, 75, 76, 77];
 assert (fillmissing (x, "constant", [88:99], 3, "endvalues", [68:79]), y);
 assert (fillmissing (x, "constant", [88:93; 94:99]', 3, "endvalues", [68:73; 74:79]'), y)
***** test
 x = reshape ([1:24],4,3,2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
 assert (fillmissing (x, "constant", [92:99], 2), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [88:99], 3), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [76:99], 99), y);
***** assert (fillmissing ([1, 2, 3], "previous"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "next"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "previous"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, 3]', "next"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "previous"), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "next"), [1, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "previous"), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "next"), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3], "previous"), [1, 1, 3])
***** assert (fillmissing ([1, NaN, 3], "next"), [1, 3, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 1), [1, 2, NaN; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 2), [1, 2, 2; 4, 4, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 1), [1, 2, 6; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 2), [1, 2, NaN; 4, 6, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 3), [1, 2, NaN; 4, NaN, 6])
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 14, 19, 22, 23]) = [2, 8, 8, 10, 15, 20, 24, 24];
 assert (fillmissing (x, "next", 1), y);
 y = x;
 y([1, 6, 7, 14, 16]) = [5, 10, 11, 18, 20];
 assert (fillmissing (x, "next", 2), y);
 y = x;
 y([1, 6, 9, 12]) = [13, 18, 21, 24];
 assert (fillmissing (x, "next", 3), y);
 assert (fillmissing (x, "next", 99), x);
 y = x;
 y([6, 7, 12, 14, 16, 19, 22, 23]) = [5, 5, 11, 13, 15, 18, 21, 21];
 assert (fillmissing (x, "previous", 1), y);
 y = x;
 y([6, 7, 9, 12, 19, 22, 23]) = [2, 3, 5, 8, 15, 18, 15];
 assert (fillmissing (x, "previous", 2), y);
 y = x;
 y([14, 16, 22, 23]) = [2, 4, 10, 11];
 assert (fillmissing (x, "previous", 3), y);
 assert (fillmissing (x, "previous", 99), x);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "previous"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "next"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "previous"), [1, 0, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "next"), [1, 0, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "previous"), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "next"), [1, 2, NaN])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "previous"), [1, 1, 1])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "next"), [1, NaN, NaN])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "previous"), [NaN, 2, 3])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "next"), [2, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "previous"), [NaN, NaN, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "next"), [3, 3, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "previous"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "next"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "next"), [2, 2, 0, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "next"), [2, 2, 0, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([5, 6, 8, 18]) = [4, 4, 0, 17];
 assert (fillmissing (x, "constant", 0, "endvalues", "previous"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "previous"), y);
 y = x;
 y([6, 10, 18, 20, 21]) = [0, 7, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "previous"), y);
 y = x;
 y([16, 19, 21]) = [4, 7, 9];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "previous"), y);
 assert (fillmissing (x, "constant", 0, 4, "endvalues", "previous"), x);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "previous"), x);
 y = x;
 y([1, 2, 8, 10, 13, 16, 22]) = [3, 3, 0, 11, 14, 17, 23];
 assert (fillmissing (x, "constant", 0, "endvalues", "next"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "next"), y);
 y = x;
 y([1, 2, 5, 6, 8, 18, 20, 21]) = [4, 11, 11, 0, 11, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "next"), y);
 y = x;
 y([2, 5]) = [14, 17];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "next"), y);
 assert (fillmissing (x, "constant", 0, 4, "endvalues", "next"), x);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "next"), x);
***** assert (fillmissing ([1, 2, 3], "nearest"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "nearest"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "nearest"), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "nearest"), [2, 2, 2])
***** assert (fillmissing ([1, NaN, 3], "nearest"), [1, 3, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 2), [1, 2, 2; 4, 6, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest"), [1, 3, 3, 5, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0, 1, 2, 3, 4]), [1, 3, 3, 5, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0.5, 1, 2, 3, 5]), [1, 1, 3, 3, 5])
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [2, 5, 8, 10, 11, 15, 15, 20, 21, 24];
 assert (fillmissing (x, "nearest", 1), y);
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [5, 10, 11, 5, 8, 18, 20, 15, 18, 15];
 assert (fillmissing (x, "nearest", 2), y);
 y = x;
 y([1, 6, 9, 12, 14, 16, 22, 23]) = [13, 18, 21, 24, 2, 4, 10, 11];
 assert (fillmissing (x, "nearest", 3), y);
 assert (fillmissing (x, "nearest", 99), x);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "nearest"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "nearest"), [1 0 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "nearest"), [1, 2, 2])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "nearest"), [1, 1, 1])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "nearest"), [2, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "nearest"), [3, 3, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "nearest"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "nearest"), [2, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "nearest"), [2, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 0, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "constant", 0, "endvalues", "nearest"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "nearest"), y);
 y = x;
 y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 0, 11, 7, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "nearest"), y);
 y = x;
 y([2, 5, 16, 19, 21]) = [14, 17, 4, 7, 9];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "nearest"), y);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "nearest"), x);
***** assert (fillmissing ([1, 2, 3], "linear"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "linear"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "linear"), [1, 2, 3])
***** assert (fillmissing ([NaN, 2, NaN], "linear"), [NaN, 2, NaN])
***** assert (fillmissing ([1, NaN, 3], "linear"), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 1), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 2), [1, 2, 3; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear"), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1, 2, 3, 4]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1.5, 2, 5, 14]), [1, 2.5, 3, 3.5, 5], eps)
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 1), reshape ([1:24], 4, 3, 2));
 y = reshape ([1:24], 4, 3, 2);
 y([1, 9, 14, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 2), y);
 y = reshape ([1:24], 4, 3, 2);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 3), y);
 assert (fillmissing (x, "linear", 99), x);
***** assert (fillmissing ([1, 2, 3], "linear", "endvalues", 0), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "linear", "endvalues", 0), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "linear", "endvalues", 0), [1, 2, 0])
***** assert (fillmissing ([1, NaN, NaN], "linear", "endvalues", 0), [1, 0, 0])
***** assert (fillmissing ([NaN, 2, 3], "linear", "endvalues", 0), [0, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "linear", "endvalues", 0), [0, 0, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "linear", "endvalues", 0), [0, 0, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", "endvalues", 0), [0, 2, 3, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 1, "endvalues", 0), [0, 2, 0, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 2, "endvalues", 0), [0, 2, 3, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 3, "endvalues", 0), [0, 2, 0, 4, 0])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 0;
 y(8) = 8;
 assert (fillmissing (x, "linear", "endvalues", 0), y);
 assert (fillmissing (x, "linear", 1, "endvalues", 0), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 0;
 y([6, 18, 20, 21]) = [6, 18, 20, 21];
 assert (fillmissing (x, "linear", 2, "endvalues", 0), y);
 y = x;
 y(isnan(y)) = 0;
 assert (fillmissing (x, "linear", 3, "endvalues", 0), y);
 assert (fillmissing (x, "linear", 99, "endvalues", 0), y);
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "linear"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "linear"), [1, 99, 3])
***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "linear"), [1, 99, 3, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear"), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "linear"), [NaN, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 6, 10, 18, 20, 21]) = [2.5, 5, 8.5, 17.25, 21, 21.75];
 assert (fillmissing (x, "linear", 2, "samplepoints", [2 4 8 10]), y, eps);
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.5, 8.5, 17.25, 21.5, 21.75];
 assert (fillmissing (x, "spline", 2, "samplepoints", [2, 4, 8, 10]), y, eps);
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.559386973180077, 8.5, 17.25, 21.440613026819925, 21.75];
 assert (fillmissing (x, "pchip", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
***** test <60965>
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.609523809523809, 8.5, 17.25, 21.390476190476186, 21.75];
 assert (fillmissing (x, "makima", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
!!!!! known bug: https://octave.org/testfailure/?60965
interp1: invalid METHOD 'makima'
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "spline"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "spline"), [1, 99, 3])
***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "spline"), [1, 99, 3, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline"), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "spline"), [NaN, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
***** assert (fillmissing ([1, 2, 3], "movmean", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "movmean", 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], "movmean", 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "movmean", [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "movmean", 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "movmean", 2), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]'), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", 2), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [1, 0]), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1]), [2, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1.1]), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", [3, 0]), [1, 1, 3, 2, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 1), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 1), [1, 3, 3, 3, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 2), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 2), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1 2, 3, 4, 4.5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 4.5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 4.5]), [1, 1, 5, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2 3, 4, 4.5]), [1, 1, 3, 5, 5])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "movmean", 3), y);
 assert (fillmissing (x, "movmean", [1, 1]), y);
 assert (fillmissing (x, "movmean", 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmean", 3, "samplepoints", [1, 2, 3]), y);
 y = x;
 y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
 assert (fillmissing (x, "movmean", 3, 2), y);
 assert (fillmissing (x, "movmean", [1, 1], 2), y);
 assert (fillmissing (x, "movmean", 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y([1, 18]) = NaN;
 y(6) = 9;
 assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, "movmean", 3, "endvalues", 99), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
 y([6, 18, 20, 21]) = [6, 15, 20, 24];
 assert (fillmissing (x, "movmean", 3, 2, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], "movmedian", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "movmedian", 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], "movmedian", 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "movmedian", [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "movmedian", 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "movmedian", 2), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]'), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", 2), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [1, 0]), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1]), [2, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1.1]), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", [3, 0]), [1, 1, 3, 2, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 1), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 1), [1, 3, 3, 3, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 2), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 2), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1 2 3 4 4.5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1 2 3 4 4.5]), [1, 1, 5, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 3, 5, 5])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "movmedian", 3), y);
 assert (fillmissing (x, "movmedian", [1, 1]), y);
 assert (fillmissing (x, "movmedian", 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmedian", 3, "samplepoints", [1, 2, 3]), y);
 y = x;
 y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
 assert (fillmissing (x, "movmedian", 3, 2), y);
 assert (fillmissing (x, "movmedian", [1, 1], 2), y);
 assert (fillmissing (x, "movmedian", 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y([1,18]) = NaN;
 y(6) = 9;
 assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, "movmedian", 3, "endvalues", 99), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
 y([6, 18, 20, 21]) = [6, 15, 20, 24];
 assert (fillmissing (x, "movmedian", 3, 2, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', @(x,y,z) x+y+z, 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 2), [1, 2, 7])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [1, 2, 7])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]'), [1, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, 2), [5, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [NaN, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1]), [5, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1.1]), [5, 2, NaN])
***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 2), [1, 2, 7, 12, 3, 4])
***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 0.5), [1, 2, NaN, NaN, 3, 4])
***** function A = testfcn (x, y, z)
  if (isempty (y))
    A = z;
  elseif (numel (y) == 1)
    A = repelem (x(1), numel(z));
  else
    A = interp1 (y, x, z, "linear", "extrap");
  endif
***** endfunction
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, [3, 0]), [1, 1, 3, NaN, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 1), [1, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 1), [1, 2, 3, 4, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 2), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 2), [1, NaN, 3, NaN, 5]') ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]'
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 3), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 3), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 2.5, 3, 3.5]), [1, 2.6, 3.4, 4.2, 5], 10*eps)
***** assert (fillmissing ([NaN, NaN, 3, NaN, 5], @testfcn, 99, 1), [NaN, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
***** test
***** function A = testfcn (x, y, z)
  if (isempty (y))
    A = z;
  elseif (numel (y) == 1)
    A = repelem (x(1), numel(z));
  else
    A = interp1 (y, x, z, "linear", "extrap");
  endif
***** endfunction
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, @testfcn, 3), y);
 assert (fillmissing (x, @testfcn, [1, 1]), y);
 assert (fillmissing (x, @testfcn, 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, @testfcn, 3, "samplepoints", [1, 2, 3]), y);
 y= x;
 y(isnan (x)) = 99;
 y(8) = 8;
 assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y)
 y = x;
 y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 6, 11, 7, 18, 20, 21];
 assert (fillmissing (x, @testfcn, 3, 2), y);
 assert (fillmissing (x, @testfcn, [1, 1], 2), y);
 assert (fillmissing (x, @testfcn, 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y(1) = NaN;
 y([6, 18, 21]) = [9, 24, 24];
 assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y);
 y([6, 18, 20, 21]) = [6, 18, 20, 21];
 y(8) = 99;
 assert (fillmissing (x, @testfcn, 3, 2, "endvalues", 99), y);
 y([6, 18, 20, 21]) = 99;
 assert (fillmissing (x, @testfcn, 3, 3, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 99), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1), [1, NaN, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1.999), [1, NaN, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 2), [1, 0, 3])
***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 2), [1, NaN, NaN, 4])
***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 3), [1, 0, 0, 4])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2), [1, 0, 3, 0, 5])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 0.999), [NaN, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 1), [0, 2, 0])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 1), [0, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 2), [0, 2, 0, 0])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 1), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 3), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 999), [NaN, NaN, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5; 1, NaN, 3, NaN, 5], "constant", 0, 2, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5; 1, NaN, 3, 0, 5])
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
 assert (fillmissing (x, "constant", 0, "maxgap", 0.1), x);
 y = x;
 y([4, 7, 12]) = 0;
 assert (fillmissing (x, "constant", 0, "maxgap", 1), y);
 assert (fillmissing (x, "constant", 0, 1, "maxgap", 1), y);
 y = x;
 y([5, 7, 12]) = 0;
 assert (fillmissing (x, "constant", 0, 2, "maxgap", 1), y);
 y = x;
 y([4, 5, 7]) = 0;
 assert (fillmissing (x, "constant", 0, 3, "maxgap", 1), y);
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
 [~, idx] = fillmissing (x, "constant", 0, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 1, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 2, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 1; 0, 0, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 3, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 1; 0, 1, 0], [1, 0, 0; 0, 0, 0])));
***** test
 x = [NaN, 2, 3];
 [~, idx] = fillmissing (x, "previous");
 assert (idx, logical ([0, 0, 0]));
 [~, idx] = fillmissing (x, "movmean", 1);
 assert (idx, logical ([0, 0, 0]));
 x = [1:3; 4:6; 7:9];
 x([2, 4, 7, 9]) = NaN;
 [~, idx] = fillmissing (x, "linear");
 assert (idx, logical ([0, 1, 0; 1, 0, 0; 0, 0, 0]));
 [~, idx] = fillmissing (x, "movmean", 2);
 assert (idx, logical ([0, 0, 0; 1, 0, 0; 0, 0, 1]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",2);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",3);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2);
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmedian", 3);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) z, 3);
 assert (A, [1, 2, 1, 4, 1]);
 assert (idx, logical ([0, 1, 0, 1, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3);
 assert (A, [1, NaN, 1, NaN, 1]);
 assert (idx, logical ([0, 0, 0, 0, 0]));
***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([0, 0, 0])), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([1, 1, 1])), [99, 99, 99])
***** assert (fillmissing ([1, NaN, 2, 3, NaN], "constant", 99, "missinglocations", logical ([1, 0, 1, 0, 1])), [99, NaN, 99, 3, 99])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0])), [1, NaN, NaN, NaN, 5])
***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([0, 0, 0, 0; 0, 0, 0, 0])), ["foo "; " bar"])
***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([1, 0, 1, 0; 0, 1, 1, 0])), ["XoX "; " XXr"])
***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([0, 0, 0])), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([1, 1, 0])), {"X", "X", "bar"})
***** test
 [~, idx] = fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing ([1 NaN 3 NaN 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0]));
 assert (idx, logical ([0, 1, 1, 1, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) ones (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
 assert (A, [1, 1, 1, 1, 1]);
 assert (idx, logical ([0, 1, 0, 1, 1]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
 assert (A, [1, NaN, 1, NaN, NaN]);
 assert (idx, logical ([0, 0, 0, 0, 0]));
***** test
 [A, idx] = fillmissing ([1, 2, 5], "movmedian", 3, "missinglocations", logical ([0, 1, 0]));
 assert (A, [1, 3, 5]);
 assert (idx, logical ([0, 1, 0]));
***** assert (fillmissing (" foo bar ", "constant", "X"), "XfooXbarX")
***** assert (fillmissing ([" foo"; "bar "], "constant", "X"), ["Xfoo"; "barX"])
***** assert (fillmissing ([" foo"; "bar "], "next"), ["bfoo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "next", 1), ["bfoo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "previous"), [" foo"; "baro"])
***** assert (fillmissing ([" foo"; "bar "], "previous", 1), [" foo"; "baro"])
***** assert (fillmissing ([" foo"; "bar "], "nearest"), ["bfoo"; "baro"])
***** assert (fillmissing ([" foo"; "bar "], "nearest", 1), ["bfoo"; "baro"])
***** assert (fillmissing ([" foo"; "bar "], "next", 2), ["ffoo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "previous", 2), [" foo"; "barr"])
***** assert (fillmissing ([" foo"; "bar "], "nearest", 2), ["ffoo"; "barr"])
***** assert (fillmissing ([" foo"; "bar "], "next", 3), [" foo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "previous", 3), [" foo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "nearest", 3), [" foo"; "bar "])
***** assert (fillmissing ({"foo", "bar"}, "constant", "a"), {"foo", "bar"})
***** assert (fillmissing ({"foo", "bar"}, "constant", {"a"}), {"foo", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "constant", "a"), {"foo", "a", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "constant", {"a"}), {"foo", "a", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "previous"), {"foo", "foo", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "next"), {"foo", "bar", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "nearest"), {"foo", "bar", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "previous", 2), {"foo", "foo", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "next", 2), {"foo", "bar", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 2), {"foo", "bar", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "next", 1), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 1), {"foo", "", "bar"})
***** assert (fillmissing ("abc ", @(x,y,z) x+y+z, 2), "abcj")
***** assert (fillmissing ({"foo", "", "bar"}, @(x,y,z) x(1), 3), {"foo", "foo", "bar"})
***** test
 [A, idx] = fillmissing (" a b c", "constant", " ");
 assert (A, " a b c");
 assert (idx, logical ([0, 0, 0, 0, 0, 0]));
 [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", "");
 assert (A, {"foo", "", "bar", ""});
 assert (idx, logical ([0, 0, 0, 0]));
 [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", {""});
 assert (A, {"foo", "", "bar", ""});
 assert (idx, logical ([0, 0, 0, 0]));
 [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem ("a", numel (z)), 3);
 assert (A, "afaoaoa");
 assert (idx, logical ([1, 0, 1, 0, 1, 0, 1]));
 [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem (" ", numel (z)), 3);
 assert (A, " f o o ");
 assert (idx, logical ([0, 0, 0, 0, 0, 0, 0]));
 [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({"a"}, numel (z)), 3);
 assert (A, {"a", "foo", "a"});
 assert (idx, logical ([1, 0, 1]));
 [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({""}, numel (z)), 3);
 assert (A, {"", "foo", ""});
 assert (idx, logical ([0, 0, 0]));
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", true), logical ([1, 0, 1, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "previous",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "next",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3), logical ([1, 0, 1, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
***** test
 x = logical ([1, 0, 1, 0, 1]);
 [~, idx] = fillmissing (x, "constant", true);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "constant", true, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "next",  "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3);
 assert (idx, logical ([0, 0, 0, 0, 0]))
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]))
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0), int32 ([1, 2, 3, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([0, 2, 0, 4, 0]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "previous", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 2, 4, 4]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "next", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 4]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3), int32 ([1, 2, 3, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([11, 2, 13, 4, 15]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 13, 4, 15]))
***** test
 x = int32 ([1, 2, 3, 4, 5]);
 [~, idx] = fillmissing (x, "constant", 0);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "constant", 3, "missinglocations", logical ([0, 0, 1, 0, 0]));
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "next", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, 3);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
***** test
 [A, idx] = fillmissing ([struct, struct], "constant", 1);
 assert (A, [struct, struct])
 assert (idx, [false, false])
***** error <Invalid call> fillmissing ()
***** error <Invalid call> fillmissing (1)
***** error <Invalid call> fillmissing (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
***** error <second input must be a> fillmissing (1, 2)
***** error <unknown fill method 'foo'> fillmissing (1, "foo")
***** error <fill function must accept at least> fillmissing (1, @(x) x, 1)
***** error <fill function must accept at least> fillmissing (1, @(x,y) x+y, 1)
***** error <interpolation methods only valid for numeric> fillmissing ("a b c", "linear")
***** error <interpolation methods only valid for numeric> fillmissing ({"a", "b"}, "linear")
***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ("a b c", "movmean", 2)
***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ({"a", "b"}, "movmean", 2)
***** error <'constant' method must be followed by> fillmissing (1, "constant")
***** error <a numeric fill value cannot be emtpy> fillmissing (1, "constant", [])
***** error <fill value must be the same data type> fillmissing (1, "constant", "a")
***** error <fill value must be the same data type> fillmissing ("a", "constant", 1)
***** error <fill value must be the same data type> fillmissing ("a", "constant", {"foo"})
***** error <fill value must be the same data type> fillmissing ({"foo"}, "constant", 1)
***** error <moving window method must be followed by> fillmissing (1, "movmean")
***** error <moving window method must be followed by> fillmissing (1, "movmedian")
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, 0)
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, -1)
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, [1, 2])
***** error <properties must be given as> fillmissing (1, "constant", 1, "samplepoints")
***** error <properties must be given as> fillmissing (1, "constant", 1, "foo")
***** error <properties must be given as> fillmissing (1, "constant", 1, 1, "foo")
***** error <invalid parameter name specified> fillmissing (1, "constant", 1, 2, {1}, 4)
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [3, 1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", "abc")
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", logical ([1, 1, 1]))
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 1, "samplepoints", [1, 2, 3])
***** error <EndValues method 'constant' only valid> fillmissing ("foo", "next", "endvalues", 1)
***** error <invalid EndValues method 'foo'> fillmissing (1, "constant", 1, 1, "endvalues", "foo")
***** error <EndValues must be a scalar or a 1 element array> fillmissing ([1, 2, 3], "constant", 1, 2, "endvalues", [1, 2, 3])
***** error <EndValues must be a scalar or a 3 element array> fillmissing ([1, 2, 3], "constant", 1, 1, "endvalues", [1, 2])
***** error <EndValues must be a scalar or a 12 element array> fillmissing (randi(5,4,3,2), "constant", 1, 3, "endvalues", [1, 2])
***** error <EndValues must be numeric or a> fillmissing (1, "constant", 1, 1, "endvalues", {1})
***** error <invalid parameter name 'foo'> fillmissing (1, "constant", 1, 2, "foo", 4)
***** error <MissingLocations option is not compatible with> fillmissing (struct, "constant", 1, "missinglocations", false)
***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "maxgap", 1, "missinglocations", false)
***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "missinglocations", false, "maxgap", 1)
***** error <the 'replacevalues' option has not> fillmissing (1, "constant", 1, "replacevalues", true)
***** error <the 'datavariables' option has not> fillmissing (1, "constant", 1, "datavariables", "Varname")
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", 1)
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", "a")
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", [true, false])
***** error <MissingLocations cannot be used with method> fillmissing (true, "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with method> fillmissing (int8 (1), "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "missinglocations", true, "EndValues", "linear")
***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "EndValues", "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "missinglocations", true, "EndValues", "linear")
***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "EndValues", "linear", "missinglocations", true)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", true)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", "a")
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", [1, 2])
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", 0)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", -1)
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3])
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3])
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3], 1)
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3], 2)
***** error <fill value 'V' must be a scalar or a 6> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 1)
***** error <fill value 'V' must be a scalar or a 8> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 2)
***** error <fill value 'V' must be a scalar or a 12> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 3)
***** error <fill function handle must be followed by> fillmissing (1, @(x,y,z) x+y+z)
***** error <fill function return values must be the same size> fillmissing ([1, NaN, 2], @(x,y,z) [1, 2], 2)
380 tests, 379 passed, 0 known failure, 1 skipped
[inst/canoncorr.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/canoncorr.m
***** shared X,Y,A,B,r,U,V,k
 k = 10;
 X = [1:k; sin(1:k); cos(1:k)]'; Y = [tan(1:k); tanh((1:k)/k)]';
 [A,B,r,U,V,stats] = canoncorr (X,Y);
***** assert (A, [-0.329229   0.072908; 0.074870   1.389318; -0.069302  -0.024109], 1E-6);
***** assert (B, [-0.017086  -0.398402; -4.475049  -0.824538], 1E-6);
***** assert (r, [0.99590   0.26754], 1E-5);
***** assert (U, center(X) * A, 10*eps);
***** assert (V, center(Y) * B, 10*eps);
***** assert (cov(U), eye(size(U, 2)), 10*eps);
***** assert (cov(V), eye(size(V, 2)), 10*eps);
 rand ("state", 1); [A,B,r] = canoncorr (rand(5, 10),rand(5, 20));
***** assert (r, ones(1, 5), 10*eps);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/regress.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/regress.m
***** test
 % Longley data from the NIST Statistical Reference Dataset
 Z = [  60323    83.0   234289   2356     1590    107608  1947
        61122    88.5   259426   2325     1456    108632  1948
        60171    88.2   258054   3682     1616    109773  1949
        61187    89.5   284599   3351     1650    110929  1950
        63221    96.2   328975   2099     3099    112075  1951
        63639    98.1   346999   1932     3594    113270  1952
        64989    99.0   365385   1870     3547    115094  1953
        63761   100.0   363112   3578     3350    116219  1954
        66019   101.2   397469   2904     3048    117388  1955
        67857   104.6   419180   2822     2857    118734  1956
        68169   108.4   442769   2936     2798    120445  1957
        66513   110.8   444546   4681     2637    121950  1958
        68655   112.6   482704   3813     2552    123366  1959
        69564   114.2   502601   3931     2514    125368  1960
        69331   115.7   518173   4806     2572    127852  1961
        70551   116.9   554894   4007     2827    130081  1962 ];
 % Results certified by NIST using 500 digit arithmetic
 % b and standard error in b
 V = [  -3482258.63459582         890420.383607373
         15.0618722713733         84.9149257747669
        -0.358191792925910E-01    0.334910077722432E-01
        -2.02022980381683         0.488399681651699
        -1.03322686717359         0.214274163161675
        -0.511041056535807E-01    0.226073200069370
         1829.15146461355         455.478499142212 ];
 Rsq = 0.995479004577296;
 F = 330.285339234588;
 y = Z(:,1); X = [ones(rows(Z),1), Z(:,2:end)];
 alpha = 0.05;
 [b, bint, r, rint, stats] = regress (y, X, alpha);
 assert(b,V(:,1),4e-6);
 assert(stats(1),Rsq,1e-12);
 assert(stats(2),F,3e-8);
 assert(((bint(:,1)-bint(:,2))/2)/tinv(alpha/2,9),V(:,2),-1.e-5);
warning: matrix singular to machine precision, rcond = 3.50566e-20
warning: called from
    regress at line 131 column 7
    __test__ at line 33 column 28
    test at line 682 column 11
    /tmp/tmp.Hr9CRkGynF at line 326 column 31

1 test, 1 passed, 0 known failure, 0 skipped
[inst/ttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ttest.m
***** test
 x = 8:0.1:12;
 [h, pval, ci] = ttest (x, 10);
 assert (h, 0)
 assert (pval, 1, 10*eps)
 assert (ci, [9.6219 10.3781], 1E-5)
 [h, pval, ci0] = ttest (x, 0);
 assert (h, 1)
 assert (pval, 0)
 assert (ci0, ci, 2e-15)
 [h, pval, ci] = ttest (x, 10, "tail", "right", "dim", 2, "alpha", 0.05);
 assert (h, 0)
 assert (pval, 0.5, 10*eps)
 assert (ci, [9.68498 Inf], 1E-5)
***** error ttest ([8:0.1:12], 10, "tail", "invalid");
***** error ttest ([8:0.1:12], 10, "tail", 25);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/Regression/RegressionGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Regression/RegressionGAM.m
***** demo
 ## Train a RegressionGAM Model for synthetic values
 f1 = @(x) cos (3 * x);
 f2 = @(x) x .^ 3;
 x1 = 2 * rand (50, 1) - 1;
 x2 = 2 * rand (50, 1) - 1;
 y = f1(x1) + f2(x2);
 y = y + y .* 0.2 .* rand (50,1);
 X = [x1, x2];
 a = fitrgam (X, y, "tol", 1e-3)
***** demo
 ## Declare two different functions
 f1 = @(x) cos (3 * x);
 f2 = @(x) x .^ 3;

 ## Generate 80 samples for f1 and f2
 x = [-4*pi:0.1*pi:4*pi-0.1*pi]';
 X1 = f1 (x);
 X2 = f2 (x);

 ## Create a synthetic response by adding noise
 rand ("seed", 3);
 Ytrue = X1 + X2;
 Y = Ytrue + Ytrue .* 0.2 .* rand (80,1);

 ## Assemble predictor data
 X = [X1, X2];

 ## Train the GAM and test on the same data
 a = fitrgam (X, Y, "order", [5, 5]);
 [ypred, ySDsd, yInt] = predict (a, X);

 ## Plot the results
 figure
 [sortedY, indY] = sort (Ytrue);
 plot (sortedY, "r-");
 xlim ([0, 80]);
 hold on
 plot (ypred(indY), "g+")
 plot (yInt(indY,1), "k:")
 plot (yInt(indY,2), "k:")
 xlabel ("Predictor samples");
 ylabel ("Response");
 title ("actual vs predicted values for function f1(x) = cos (3x) ");
 legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});

 ## Use 30% Holdout partitioning for training and testing data
 C = cvpartition (80, "HoldOut", 0.3);
 [ypred, ySDsd, yInt] = predict (a, X(test(C),:));

 ## Plot the results
 figure
 [sortedY, indY] = sort (Ytrue(test(C)));
 plot (sortedY, 'r-');
 xlim ([0, sum(test(C))]);
 hold on
 plot (ypred(indY), "g+")
 plot (yInt(indY,1),'k:')
 plot (yInt(indY,2),'k:')
 xlabel ("Predictor samples");
 ylabel ("Response");
 title ("actual vs predicted values for function f1(x) = cos (3x) ");
 legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4];
 a = RegressionGAM (x, y);
 assert ({a.X, a.Y}, {x, y})
 assert ({a.BaseModel.Intercept}, {2.5000})
 assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
 assert ({a.NumObservations, a.NumPredictors}, {4, 3})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.Formula}, {[]})
***** test
 x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
 y = [1; 2; 3; 4];
 pnames = {"A", "B", "C", "D"};
 formula = "Y ~ A + B + C + D + A:C";
 intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
 a = RegressionGAM (x, y, "predictors", pnames, "formula", formula);
 assert ({a.IntMatrix}, {intMat})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
 assert ({a.Formula}, {formula})
***** error<RegressionGAM: too few input arguments.> RegressionGAM ()
***** error<RegressionGAM: too few input arguments.> RegressionGAM (ones(10,2))
***** error<RegressionGAM: number of rows in X and Y must be equal.> ...
 RegressionGAM (ones(10,2), ones (5,1))
***** error<RegressionGAM: invalid values in X.> ...
 RegressionGAM ([1;2;3;"a";4], ones (5,1))
***** error<RegressionGAM: invalid parameter name in optional pair arguments.> ...
 RegressionGAM (ones(10,2), ones (10,1), "some", "some")
***** error<RegressionGAM: Formula must be a string.>
 RegressionGAM (ones(10,2), ones (10,1), "formula", {"y~x1+x2"})
***** error<RegressionGAM: Formula must be a string.>
 RegressionGAM (ones(10,2), ones (10,1), "formula", [0, 1, 0])
***** error<RegressionGAM: invalid syntax in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something")
***** error<RegressionGAM: no predictor terms in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
***** error<RegressionGAM: no predictor terms in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
***** error<RegressionGAM: some predictors have not been identified> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~x1:")
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", "some")
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", -1)
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", [1 2 3 4])
***** error<RegressionGAM: number of interaction terms requested is larger than> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", 3)
***** error<RegressionGAM: Formula has been already defined.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "y ~ x1 + x2", "interactions", 1)
***** error<RegressionGAM: Interactions have been already defined.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", 1, "formula", "y ~ x1 + x2")
***** error<RegressionGAM: invalid value for Knots.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", "a")
***** error<RegressionGAM: DoF and Order have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "order", 3, "dof", 2, "knots", 5)
***** error<RegressionGAM: invalid value for DoF.> ...
 RegressionGAM (ones(10,2), ones (10,1), "dof", 'a')
***** error<RegressionGAM: Knots and Order have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "order", 3, "dof", 2)
***** error<RegressionGAM: invalid value for Order.> ...
 RegressionGAM (ones(10,2), ones (10,1), "order", 'a')
***** error<RegressionGAM: DoF and Knots have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "dof", 2, "order", 2)
***** error<RegressionGAM: Tolerance must be a Positive scalar.> ...
 RegressionGAM (ones(10,2), ones (10,1), "tol", -1)
***** error<RegressionGAM: ResponseName must be a char string.> ...
 RegressionGAM (ones(10,2), ones (10,1), "responsename", -1)
***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", -1)
***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", ['a','b','c'])
***** error<RegressionGAM: PredictorNames must have same number of columns as X.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", {'a','b','c'})
***** error<RegressionGAM.predict: too few arguments.> ...
 predict (RegressionGAM (ones(10,1), ones(10,1)))
***** error<RegressionGAM.predict: Xfit is empty.> ...
 predict (RegressionGAM (ones(10,1), ones(10,1)), [])
***** error<RegressionGAM.predict: Xfit must have the same number of features> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), 2)
***** error<RegressionGAM.predict: invalid NAME in optional pairs of arguments.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "some", "some")
***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", "some")
***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", 5)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 5)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", -1)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 'a')
39 tests, 39 passed, 0 known failure, 0 skipped
[inst/@cvpartition/set.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/set.m
***** shared C
 C = cvpartition (ones (10, 1), "KFold", 5);
***** test
 Cnew = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
 assert (get (Cnew, "inds"), [1 2 2 2 3 4 3 4 5 5]');
***** error<set: expecting field/value pairs.> set (C)
***** error<set: expecting field/value pairs.> set (C, "NumObservations")
***** error<set: invalid field some.> set (C, "some", 15)
***** error<set: expecting the field to be a string.> set (C, 15, 15)
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/@cvpartition/cvpartition.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/cvpartition.m
***** demo
 ## Partition with Fisher iris dataset (n = 150)
 ## Stratified by species
 load fisheriris
 y = species;
 ## 10-fold cross-validation partition
 c = cvpartition (species, 'KFold', 10)
 ## leave-10-out partition
 c1 = cvpartition (species, 'HoldOut', 10)
 idx1 = test (c, 2);
 idx2 = training (c, 2);
 ## another leave-10-out partition
 c2 = repartition (c1)
***** test
 C = cvpartition (ones (10, 1));
 assert (isa (C, "cvpartition"), true);
***** test
 C = cvpartition (ones (10, 1), "KFold", 5);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 5);
 assert (get (C, "TrainSize"), ones(5,1) * 8);
 assert (get (C, "TestSize"), ones (5,1) * 2);
 assert (get (C, "inds"), [1 1 2 2 3 3 4 4 5 5]');
 assert (get (C, "Type"), "kfold");
***** test
 C = cvpartition (ones (10, 1), "KFold", 2);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 2);
 assert (get (C, "TrainSize"), [5; 5]);
 assert (get (C, "TestSize"), [5; 5]);
 assert (get (C, "inds"), [1 1 1 1 1 2 2 2 2 2]');
 assert (get (C, "Type"), "kfold");
***** test
 C = cvpartition (ones (10, 1), "HoldOut", 5);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 1);
 assert (get (C, "TrainSize"), 5);
 assert (get (C, "TestSize"), 5);
 assert (class (get (C, "inds")), "logical");
 assert (length (get (C, "inds")), 10);
 assert (get (C, "Type"), "holdout");
***** test
 C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "LeaveOut", 5);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 10);
 assert (get (C, "TrainSize"), ones (10, 1));
 assert (get (C, "TestSize"), ones (10, 1) * 9);
 assert (get (C, "inds"), []);
 assert (get (C, "Type"), "leaveout");
***** test
 C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "resubstitution", 5);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 1);
 assert (get (C, "TrainSize"), 10);
 assert (get (C, "TestSize"), 10);
 assert (get (C, "inds"), []);
 assert (get (C, "Type"), "resubstitution");
***** test
 C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "Given", 2);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 10);
 assert (get (C, "TrainSize"), ones (10, 1) * 9);
 assert (get (C, "TestSize"), ones (10, 1));
 assert (get (C, "inds"), [1:10]');
 assert (get (C, "Type"), "given");
***** warning<cvpartition: unrecognized type, using KFold.> ...
 C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "some", 2);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/@cvpartition/get.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/get.m
***** shared C
 C = cvpartition (ones (10, 1), "KFold", 5);
***** assert (get (C, "NumObservations"), 10);
***** assert (get (C, "NumTestSets"), 5);
***** assert (get (C, "TrainSize"), ones(5,1) * 8);
***** assert (get (C, "TestSize"), ones (5,1) * 2);
***** assert (get (C, "inds"), [1 1 2 2 3 3 4 4 5 5]');
***** assert (get (C, "Type"), "kfold");
***** error<get: invalid property some.> get (C, "some")
***** error<get: expecting the property to be a string.> get (C, 25)
***** error<get: expecting the property to be a string.> get (C, {25})
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/@cvpartition/training.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/training.m
***** shared C
 C = cvpartition (ones (10, 1), "KFold", 5);
***** assert (training (C, 1), logical ([0 0 1 1 1 1 1 1 1 1]'))
***** assert (training (C, 2), logical ([1 1 0 0 1 1 1 1 1 1]'))
***** assert (training (C, 3), logical ([1 1 1 1 0 0 1 1 1 1]'))
***** assert (training (C, 4), logical ([1 1 1 1 1 1 0 0 1 1]'))
***** assert (training (C, 5), logical ([1 1 1 1 1 1 1 1 0 0]'))
***** test
 C = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
***** assert (training (C), logical ([0 1 1 1 1 1 1 1 1 1]'))
***** assert (training (C, 2), logical ([1 0 0 0 1 1 1 1 1 1]'))
***** assert (training (C, 3), logical ([1 1 1 1 0 1 0 1 1 1]'))
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/@cvpartition/test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/test.m
***** shared C
 C = cvpartition (ones (10, 1), "KFold", 5);
***** assert (test (C, 1), logical ([1 1 0 0 0 0 0 0 0 0]'))
***** assert (test (C, 2), logical ([0 0 1 1 0 0 0 0 0 0]'))
***** assert (test (C, 3), logical ([0 0 0 0 1 1 0 0 0 0]'))
***** assert (test (C, 4), logical ([0 0 0 0 0 0 1 1 0 0]'))
***** assert (test (C, 5), logical ([0 0 0 0 0 0 0 0 1 1]'))
***** test
 C = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
***** assert (test (C), logical ([1 0 0 0 0 0 0 0 0 0]'))
***** assert (test (C, 2), logical ([0 1 1 1 0 0 0 0 0 0]'))
***** assert (test (C, 3), logical ([0 0 0 0 1 0 1 0 0 0]'))
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/@cvpartition/display.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/display.m
***** test
 C = cvpartition (ones (10, 1), "KFold", 5);
 s = evalc ("display (C)");
 sout = "K-fold cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** test
 C = cvpartition (ones (10, 1), "HoldOut", 5);
 s = evalc ("display (C)");
 sout = "HoldOut cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** test
 C = cvpartition (ones (10, 1), "LeaveOut", 5);
 s = evalc ("display (C)");
 sout = "Leave-One-Out cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** test
 C = cvpartition (ones (10, 1), "resubstitution", 5);
 s = evalc ("display (C)");
 sout = "Resubstitution cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** test
 C = cvpartition (ones (10, 1), "Given", 5);
 s = evalc ("display (C)");
 sout = "Given cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** error<Invalid call to display.  Correct usage is> display ()
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/@cvpartition/repartition.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/repartition.m
***** test
 C = cvpartition (ones (10, 1), "KFold", 5);
 Cnew = repartition (C);
 assert (isa (Cnew, "cvpartition"), true);
***** test
 C = cvpartition (ones (100, 1), "HoldOut", 5);
 Cnew = repartition (C);
 indC = get (C, "inds");
 indCnew = get (Cnew, "inds");
 assert (isequal (indC, indCnew), false);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/cdfcalc.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cdfcalc.m
***** test
 x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
 [yCDF, xCDF, n, emsg, eid] = cdfcalc (x);
 assert (yCDF, [0, 0.3, 0.5, 0.8, 0.9, 1]');
 assert (xCDF, [2, 3, 4, 5, 6]');
 assert (n, 10);
***** shared x
 x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
***** error yCDF = cdfcalc (x);
***** error [yCDF, xCDF] = cdfcalc ();
***** error [yCDF, xCDF] = cdfcalc (x, x);
***** warning [yCDF, xCDF] = cdfcalc (ones(10,2));
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/barttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/barttest.m
***** error<barttest: invalid number of input arguments.> barttest ()
***** error<barttest: NaN values in input are not allowed.> barttest ([2,NaN;3,4])
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), "alpha")
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 0)
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 1.2)
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), [0.2, 0.05])
***** error<barttest: not enough data in X.> barttest (ones (30, 1))
***** error<barttest: not enough data in X.> barttest (ones (30, 1), 0.05)
***** test
 x = [2, 3, 4, 5, 6, 7, 8, 9; 1, 2, 3, 4, 5, 6, 7, 8]';
 [ndim, pval, chisq] = barttest (x);
 assert (ndim, 2);
 assert (pval, 0);
 ## assert (chisq, 512.0558, 1e-4); Result differs between octave 6 and 7 ?
***** test
 x = [0.53767,  0.62702,   -0.10224,   -0.25485,   1.4193,   1.5237  ; ...
      1.8339,   1.6452,    -0.24145,   -0.23444,   0.29158,  0.1634  ; ...
     -2.2588,  -2.1351,     0.31286,    0.39396,   0.19781,  0.20995 ; ...
      0.86217,  1.0835,     0.31286,    0.46499,   1.5877,   1.495   ; ...
      0.31877,  0.38454,   -0.86488,   -0.63839,  -0.80447, -0.7536  ; ...
     -1.3077,  -1.1487,    -0.030051,  -0.017629,  0.69662,  0.60497 ; ...
     -0.43359, -0.32672,   -0.16488,   -0.37364,   0.83509,  0.89586 ; ...
      0.34262,  0.29639,    0.62771,    0.51672,  -0.24372, -0.13698 ; ...
      3.5784,   3.5841,     1.0933,     0.93258,   0.21567,  0.455   ; ...
      2.7694,   2.6307,     1.1093,     1.4298,   -1.1658,  -1.1816  ; ...
     -1.3499,  -1.2111,    -0.86365,   -0.94186,  -1.148,   -1.4381  ; ...
      3.0349,   2.8428,     0.077359,   0.18211,   0.10487, -0.014613; ...
      0.7254,   0.56737,   -1.2141,    -1.2291,    0.72225,  0.90612 ; ...
     -0.063055,-0.17662,   -1.1135,    -0.97701,   2.5855,   2.4084  ; ...
      0.71474,  0.29225,   -0.0068493, -0.11468,  -0.66689, -0.52466 ; ...
     -0.20497, -7.8874e-06, 1.5326,     1.3195,    0.18733,  0.20296 ; ...
     -0.12414, -0.077029,  -0.76967,   -0.96262,  -0.082494, 0.121   ; ...
      1.4897,   1.3683,     0.37138,    0.43653,  -1.933,   -2.1903  ; ...
      1.409,    1.5882,    -0.22558,   -0.24835,  -0.43897, -0.46247 ; ...
      1.4172,   1.1616,     1.1174,     1.0785,   -1.7947,  -1.9471  ];
 [ndim, pval, chisq] = barttest (x);
 assert (ndim, 3);
 assert (pval, [0; 0; 0; 0.52063; 0.34314], 1e-5);
 chisq_out = [251.6802; 210.2670; 153.1773; 4.2026; 2.1392];
 assert (chisq, chisq_out, 1e-4);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/einstein.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/einstein.m
***** demo
 einstein (0.4, 0.6)
***** demo
 einstein (0.2, 0.5)
***** demo
 einstein (0.6, 0.1)
***** test
 hf = figure ("visible", "off");
 unwind_protect
   tiles = einstein (0.4, 0.6);
   assert (isstruct (tiles), true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error<Invalid call to einstein.  Correct usage is> einstein
***** error<Invalid call to einstein.  Correct usage is> einstein (0.5)
***** error<einstein: A and B must be within the open interval> einstein (0, 0.9)
***** error<einstein: A and B must be within the open interval> einstein (0.4, 1)
***** error<einstein: A and B must be within the open interval> einstein (-0.4, 1)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/cl_multinom.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cl_multinom.m
***** demo
 CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
***** error<Invalid call to cl_multinom.  Correct usage> cl_multinom ();
***** error cl_multinom (1, 2, 3, 4, 5);
***** error<cl_multinom: argument method must be a string.> ...
 cl_multinom (1, 2, 3, 4);
***** error<cl_multinom: unknown calculation type.> ...
 cl_multinom (1, 2, 3, "some string");
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/hmmestimate.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hmmestimate.m
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, ...
             3, 3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 [transprobest, outprobest] = hmmestimate (sequence, states);
 expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
 expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
***** test
 sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
             "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
 states = {"One", "One", "Two", "Two", "Two", "One", "One", "One", "One", ...
           "One", "One", "One", "One", "One", "One", "Two", "Two", "Two", ...
           "Two", "One", "One", "One", "One", "One", "One"};
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 [transprobest, outprobest] = hmmestimate (sequence, states, "symbols", ...
                                           symbols, "statenames", statenames);
 expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
 expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
             3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 pseudotransitions = [8, 2; 4, 6];
 pseudoemissions = [2, 4, 4; 7, 2, 1];
 [transprobest, outprobest] = hmmestimate (sequence, states, ...
  "pseudotransitions", pseudotransitions, "pseudoemissions", pseudoemissions);
 expectedtransprob = [0.85714, 0.14286; 0.35294, 0.64706];
 expectedoutprob = [0.178571, 0.357143, 0.464286; ...
                    0.823529, 0.117647, 0.058824];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/fitcnet.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcnet.m
***** demo
 ## Train a Neural Network on the Fisher's Iris data set and display
 ## a confusion chart with the classification results.

 load fisheriris
 Mdl = fitcnet (meas, species);
 pred_species = resubPredict (Mdl);
 confusionchart (species, pred_species);
***** test
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 50);
 assert (class (Mdl), "ClassificationNeuralNetwork");
 assert (numel (Mdl.ModelParameters.LayerWeights), 2);
 assert (size (Mdl.ModelParameters.LayerWeights{1}), [10, 5]);
 assert (size (Mdl.ModelParameters.LayerWeights{2}), [3, 11]);
***** error<fitcnet: too few arguments.> fitcnet ()
***** error<fitcnet: too few arguments.> fitcnet (ones (4,1))
***** error<fitcnet: Name-Value arguments must be in pairs.>
 fitcnet (ones (4,2), ones (4, 1), 'LayerSizes')
***** error<fitcnet: number of rows in X and Y must be equal.>
 fitcnet (ones (4,2), ones (3, 1))
***** error<fitcnet: number of rows in X and Y must be equal.>
 fitcnet (ones (4,2), ones (3, 1), 'LayerSizes', 2)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/normalise_distribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/normalise_distribution.m
***** test
 v = normalise_distribution ([1 2 3], [], 1);
 assert (v, [0 0 0])
***** test
 v = normalise_distribution ([1 2 3], [], 2);
 assert (v, norminv ([1 3 5] / 6), 3 * eps)
***** test
 v = normalise_distribution ([1 2 3]', [], 2);
 assert (v, [0 0 0]')
***** test
 v = normalise_distribution ([1 2 3]', [], 1);
 assert (v, norminv ([1 3 5]' / 6), 3 * eps)
***** test
 v = normalise_distribution ([1 1 2 2 3 3], [], 2);
 assert (v, norminv ([3 3 7 7 11 11] / 12), 3 * eps)
***** test
 v = normalise_distribution ([1 1 2 2 3 3]', [], 1);
 assert (v, norminv ([3 3 7 7 11 11]' / 12), 3 * eps)
***** test
 A = randn ( 10 );
 N = normalise_distribution (A, @normcdf);
 assert (A, N, 10000 * eps)
***** test
 A = exprnd (1, 100);
 N = normalise_distribution (A, @(x)(expcdf (x, 1)));
 assert (mean (vec (N)), 0, 0.1)
 assert (std (vec (N)), 1, 0.1)
***** test
 A = rand (1000,1);
 N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1))});
 assert (mean (vec (N)), 0, 0.2)
 assert (std (vec (N)), 1, 0.1)
***** test
 A = [rand(1000,1), randn(1000, 1)];
 N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1)), @normcdf});
 assert (mean (N), [0, 0], 0.2)
 assert (std (N), [1, 1], 0.1)
***** test
 A = [rand(1000,1), randn(1000, 1), exprnd(1, 1000, 1)]';
 N = normalise_distribution  (A, {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x, 1))}, 2);
 assert (mean (N, 2), [0, 0, 0]', 0.2);
 assert (std (N, [], 2), [1, 1, 1]', 0.1);
***** xtest
 A = exprnd (1, 1000, 9); A (300:500, 4:6) = 17;
 N = normalise_distribution (A);
 assert (mean (N), [0 0 0 0.38 0.38 0.38 0 0 0], 0.1);
 assert (var (N), [1 1 1 2.59 2.59 2.59 1 1 1], 0.1);
***** test
***** error normalise_distribution (zeros (3, 4), ...
 {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x,1))});
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/monotone_smooth.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/monotone_smooth.m
***** error<Invalid call to monotone_smooth.  Correct usage is> ...
 monotone_smooth (1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth ("char", 1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth ({1,2,3}, 1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth (ones(20,3), 1)
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, "char")
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, {1,2,3})
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, ones(20,3))
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), [1, 2])
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), {2})
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), "char")
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/regression_ftest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/regression_ftest.m
***** error<Invalid call to regression_ftest.  Correct usage> regression_ftest ();
***** error<Invalid call to regression_ftest.  Correct usage> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]');
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 NaN]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 Inf]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 3+i]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 NaN; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 Inf; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 3+i]', [1 0.5]);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 0);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 1.2);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", [.02 .1]);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", "a");
***** error<regression_ftest: invalid Name argument.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "some", 0.05);
***** error<regression_ftest: Y must be a vector of length> ...
 regression_ftest ([1 2 3]', [2 3; 3 4]', [1 0.5]);
***** error<regression_ftest: Y must be a vector of length> ...
 regression_ftest ([1 2; 3 4]', [2 3; 3 4]', [1 0.5]);
***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], ones (2));
***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], "alpha");
***** error<regression_ftest: reduced model, RM, must have smaller length than> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [1 2]);
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/nanmin.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/nanmin.m
***** demo
 ## Find the column minimum values and their indices
 ## for matrix data with missing values.

 x = magic (3);
 x([1, 6:9]) = NaN
 [y, ind] = nanmin (x)
***** demo
 ## Find the minimum of all the values in an array, ignoring missing values.
 ## Create a 2-by-5-by-3 array x with some missing values.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN

 ## Find the minimum of the elements of x.

 y = nanmin (x, [], 'all')
***** assert (nanmin ([2, 4, NaN, 7]), 2)
***** assert (nanmin ([2, 4, NaN, -Inf]), -Inf)
***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [1, 5, 3])
***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [1, 5, 7])
***** assert (nanmin (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([1, 5, 3]))
***** shared x, y
 x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
 x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
 y = x;
 y(2,3,1) = 0.51;
***** assert (nanmin (x, [], [1, 2])(:), [-2.95; 2.05])
***** assert (nanmin (x, [], [1, 3])(:), [1.77; -0.005; NaN; -2.95])
***** assert (nanmin (x, [], [2, 3])(:), [-2.95; 0.19])
***** assert (nanmin (x, [], [1, 2, 3]), -2.95)
***** assert (nanmin (x, [], 'all'), -2.95)
***** assert (nanmin (y, [], [1, 3])(:), [1.77; -0.005; 0.51; -2.95])
***** assert (nanmin (x(1,:,1), x(2,:,1)), [1.77, -0.005, NaN, -2.95])
***** assert (nanmin (x(1,:,2), x(2,:,2)), [6.77, 4.995, NaN, 2.05])
***** assert (nanmin (y(1,:,1), y(2,:,1)), [1.77, -0.005, 0.51, -2.95])
***** assert (nanmin (y(1,:,2), y(2,:,2)), [6.77, 4.995, NaN, 2.05])
***** test
 xx = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (nanmin (xx, [], [3, 2])), [10, 1, 1, 3]);
 assert (size (nanmin (xx, [], [1, 2])), [1, 1, 6, 3]);
 assert (size (nanmin (xx, [], [1, 2, 4])), [1, 1, 6]);
 assert (size (nanmin (xx, [], [1, 4, 3])), [1, 40]);
 assert (size (nanmin (xx, [], [1, 2, 3, 4])), [1, 1]);
***** assert (nanmin (ones (2), [], 3), ones (2, 2))
***** assert (nanmin (ones (2, 2, 2), [], 99), ones (2, 2, 2))
***** assert (nanmin (magic (3), [], 3), magic (3))
***** assert (nanmin (magic (3), [], [1, 3]), [3, 1, 2])
***** assert (nanmin (magic (3), [], [1, 99]), [3, 1, 2])
***** assert (nanmin (ones (2), 3), ones (2,2))
***** error <nanmin: VECDIM must contain non-repeating positive integers.> ...
 nanmin (y, [], [1, 1, 2])
***** error <nanmin: a second output is not supported with this syntax.> ...
 [v, idx] = nanmin(x, y, [1 2])
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/shadow9/std.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/shadow9/std.m
***** assert (std (13), 0)
***** assert (std (single (13)), single (0))
***** assert (std ([1,2,3]), 1)
***** assert (std ([1,2,3], 1), sqrt (2/3), eps)
***** assert (std ([1,2,3], [], 1), [0,0,0])
***** assert (std ([1,2,3], [], 3), [0,0,0])
***** assert (std (5, 99), 0)
***** assert (std (5, 99, 1), 0)
***** assert (std (5, 99, 2), 0)
***** assert (std ([5 3], [99 99], 2), 1)
***** assert (std ([1:7], [1:7]), sqrt (3))
***** assert (std ([eye(3)], [1:3]), sqrt ([5/36, 2/9, 1/4]), eps)
***** assert (std (ones (2,2,2), [1:2], 3), [(zeros (2,2))])
***** assert (std ([1 2; 3 4], 0, 'all'), std ([1:4]))
***** assert (std (reshape ([1:8], 2, 2, 2), 0, [1 3]), sqrt ([17/3 17/3]), eps)
***** assert (std ([1 2 3;1 2 3], [], [1 2]), sqrt (0.8), eps)
***** test
 x = [-10:10];
 y = [x;x+5;x-5];
 assert (std (x), sqrt (38.5), 1e-14);
 assert (std (y, [], 2), sqrt ([38.5; 38.5; 38.5]), 1e-14);
 assert (std (y, 0, 2), sqrt ([38.5; 38.5; 38.5]), 1e-14);
 assert (std (y, 1, 2), ones (3,1) * sqrt (36.66666666666666), 1e-14);
 assert (std (y, "all"), sqrt (54.19354838709678), 1e-14);
 y(2,4) = NaN;
 assert (std (y, "all"), NaN);
 assert (std (y, "all", "includenan"), NaN);
 assert (std (y, "all", "omitnan"), sqrt (55.01533580116342), 1e-14);
 assert (std (y, 0, 2, "includenan"), sqrt ([38.5; NaN; 38.5]), 1e-14);
 assert (std (y, [], 2), sqrt ([38.5; NaN; 38.5]), 1e-14);
 assert (std (y, [], 2, "omitnan"), ...
         sqrt ([38.5; 37.81842105263158; 38.5]), 1e-14);
***** assert (std ([4 NaN 6], [1 2 1], "omitnan"), 1, eps)
***** assert (std ([4 5 6], [1 NaN 1], "omitnan"), 1, eps)
***** assert (std (magic(3), [1 NaN 3], "omitnan"), sqrt(3)*[1 2 1], eps)
***** assert (std ([4 NaN 6], [1 2 1], "omitnan", "all"), 1, eps)
***** assert (std ([4 NaN 6], [1 2 1], "all", "omitnan"), 1, eps)
***** assert (std ([4 5 6], [1 NaN 1], "omitnan", "all"), 1, eps)
***** assert (std ([4 NaN 6], [1 2 1], 2, "omitnan"), 1, eps)
***** assert (std ([4 5 6], [1 NaN 1], 2, "omitnan"), 1, eps)
***** assert (std (magic(3), [1 NaN 3], 1, "omitnan"), sqrt(3)*[1 2 1], eps)
***** assert (std (magic(3), [1 NaN 3], 2, "omitnan"), sqrt(3)*[0.5;1;0.5], eps)
***** assert (std (4*[4 5; 6 7; 8 9], [1 3], 2, 'omitnan'), sqrt(3)*[1;1;1], eps)
***** assert (std ([4 NaN; 6 7; 8 9], [1 1 3], 1, 'omitnan'), [1.6 sqrt(3)/2], eps)
***** assert (std (4*[4 NaN; 6 7; 8 9], [1 3], 2, 'omitnan'), sqrt(3)*[0;1;1], eps)
***** assert (std (3*reshape(1:18, [3 3 2]), [1 2 3], 1, 'omitnan'), ...
          sqrt(5)*ones(1,3,2), eps)
***** assert (std (reshape(1:18, [3 3 2]), [1 2 3], 2, 'omitnan'), ...
          sqrt(5)*ones(3,1,2), eps)
***** assert (std (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 2], 'omitnan'), ...
          sqrt(60)*ones(1,1,2),eps)
***** assert (std (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 4], 'omitnan'), ...
          sqrt(6)*ones(1,3,2),eps)
***** assert (std (6*reshape(1:18, [3 3 2]), ones (3,3,2), [1:3], 'omitnan'), ...
          sqrt(969),eps)
***** test
 x = reshape(1:18, [3 3 2]);
 x([2, 14]) = NaN;
 w = ones (3,3,2);
 assert (std (16*x, w, [1:3], 'omitnan'), sqrt(6519), eps);
***** test
 x = reshape(1:18, [3 3 2]);
 w = ones (3,3,2);
 w([2, 14]) = NaN;
 assert (std (16*x, w, [1:3], 'omitnan'), sqrt(6519), eps);
***** assert (std ([1 2 3], "aLl"), 1);
***** assert (std ([1 2 3], "OmitNan"), 1);
***** assert (std ([1 2 3], "IncludeNan"), 1);
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 assert (size (std (x, 0, [3 2])), [10, 1, 1, 3]);
 assert (size (std (x, 1, [1 2])), [1, 1, 6, 3]);
 assert (size (std (x, [], [1 2 4])), [1, 1, 6]);
 assert (size (std (x, 0, [1 4 3])), [1, 40]);
 assert (size (std (x, [], [1 2 3 4])), [1, 1]);
***** assert (std (3*magic(3)), sqrt([63 144 63]), eps)
***** assert (std (3*magic(3), 'omitnan'), sqrt([63 144 63]), eps)
***** assert (std (3*magic(3), 1), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), 1, 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(1,3), 1), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(1,3), 1, 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (2*magic(3), [1 1 NaN], 1, 'omitnan'), [5 4 1], eps)
***** assert (std (3*magic(3), ones(3,3)), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(3,3), 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 'omitnan'), ...
         sqrt([42 36 42]), eps)
***** assert (std (3*magic(3), ones(3,3), 1), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(3,3), 1, 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 1, 'omitnan'), ...
         sqrt([42 36 42]), eps)
***** assert (std (3*magic(3), ones(3,3), [1 4]), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(3,3), [1 4], 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1],[1 4],'omitnan'), ...
         sqrt([42 36 42]), eps)
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 v = repmat (sqrt (33.38912133891213), [10, 1, 1, 3]);
 assert (std (x, 0, [3, 2]), v, 1e-14);
 v = repmat (sqrt (33.250), [10, 1, 1, 3]);
 assert (std (x, 1, [3, 2]), v, 1e-14);
 x(2,5,6,3) = NaN;
 v(2,1,1,3) = NaN;
 assert (std (x, 1, [3, 2]), v, 1e-14);
 v = repmat (sqrt (33.38912133891213), [10 1 1 3]);
 v(2,1,1,3) = NaN;
 assert (std (x, [], [3, 2]), v, 1e-14);
 v(2,1,1,3) = sqrt (33.40177912169048);
 assert (std (x, [], [3, 2], "omitnan"), v, 1e-14);
***** assert (std (ones (2,2,2), [1:2], 3), [(zeros (2, 2))])
***** assert (std (magic (3), [1:9], "all"), 2.581988897471611, 1e-14)
***** assert (std (ones (2,2), [], 3), zeros (2,2))
***** assert (std (ones (2,2,2), [], 99), zeros (2,2,2))
***** assert (std (magic (3), [], 3), zeros (3,3))
***** assert (std (magic (3), [], 1), sqrt ([7, 16, 7]))
***** assert (std (magic (3), [], [1 3]), sqrt ([7, 16, 7]))
***** assert (std (magic (3), [], [1 99]), sqrt ([7, 16, 7]))
***** assert (std ([]), NaN)
***** assert (class (var (single ([]))), "single")
***** assert (std ([],[],1), NaN(1,0))
***** assert (std ([],[],2), NaN(0,1))
***** assert (std ([],[],3), [])
***** assert (class (var (single ([]), [], 1)), "single")
***** assert (std (ones (1,0)), NaN)
***** assert (std (ones (1,0), [], 1), NaN(1,0))
***** assert (std (ones (1,0), [], 2), NaN)
***** assert (std (ones (1,0), [], 3), NaN(1,0))
***** assert (class (var (ones (1, 0, "single"), [], 1)), "single")
***** assert (std (ones (0,1)), NaN)
***** assert (std (ones (0,1), [], 1), NaN)
***** assert (std (ones (0,1), [], 2), NaN(0,1))
***** assert (std (ones (0,1), [], 3), NaN(0,1))
***** assert (std (ones (1,3,0,2)), NaN(1,1,0,2))
***** assert (std (ones (1,3,0,2), [], 1), NaN(1,3,0,2))
***** assert (std (ones (1,3,0,2), [], 2), NaN(1,1,0,2))
***** assert (std (ones (1,3,0,2), [], 3), NaN(1,3,1,2))
***** assert (std (ones (1,3,0,2), [], 4), NaN(1,3,0))
***** test
 [~, m] = std ([]);
 assert (m, NaN);
***** test <*62395>
 [~, m] = std (13);
 assert (m, 13);
 [~, m] = std (single(13));
 assert (m, single(13));
 [~, m] = std ([1, 2, 3; 3 2 1], []);
 assert (m, [2 2 2]);
 [~, m] = std ([1, 2, 3; 3 2 1], [], 1);
 assert (m, [2 2 2]);
 [~, m] = std ([1, 2, 3; 3 2 1], [], 2);
 assert (m, [2 2]');
 [~, m] = std ([1, 2, 3; 3 2 1], [], 3);
 assert (m, [1 2 3; 3 2 1]);
***** test <*62395>
 [~, m] = std (5,99);
 assert (m, 5);
 [~, m] = std ([1:7], [1:7]);
 assert (m, 5);
 [~, m] = std ([eye(3)], [1:3]);
 assert (m, [1/6, 1/3, 0.5], eps);
 [~, m] = std (ones (2,2,2), [1:2], 3);
 assert (m, ones (2,2));
 [~, m] = std ([1 2; 3 4], 0, 'all');
 assert (m, 2.5, eps);
 [~, m] = std (reshape ([1:8], 2, 2, 2), 0, [1 3]);
 assert (m, [3.5, 5.5], eps);
***** test
 [v, m] = std (4 * eye (2), [1, 3]);
 assert (v, sqrt ([3, 3]), 1e-14);
 assert (m, [1, 3]);
***** test <*62395>
 [~, m] = std ([]);
 assert (m, NaN);
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 [~, m] = std (x, 0, [3 2]);
 assert (m, mean (x, [3 2]));
 [~, m] = std (x, 0, [1 2]);
 assert (m, mean (x, [1 2]));
 [~, m] = std (x, 0, [1 3 4]);
 assert (m, mean (x, [1 3 4]));
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 x(2,5,6,3) = NaN;
 [~, m] = std (x, 0, [3 2], "omitnan");
 assert (m, mean (x, [3 2], "omitnan"));
***** test <*63203>
 [v, m] = std (Inf);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = std (NaN);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, Inf, 3]);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = std ([1, Inf, 3]');
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = std ([1, NaN, 3]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, NaN, 3]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, Inf, 3], [], 1);
 assert (v, [0, NaN, 0]);
 assert (m, [1, Inf, 3]);
***** test <*63203>
 [v, m] = std ([1, Inf, 3], [], 2);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = std ([1, Inf, 3], [], 3);
 assert (v, [0, NaN, 0]);
 assert (m, [1, Inf, 3]);
***** test <*63203>
 [v, m] = std ([1, NaN, 3], [], 1);
 assert (v, [0, NaN, 0]);
 assert (m, [1, NaN, 3]);
***** test <*63203>
 [v, m] = std ([1, NaN, 3], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, NaN, 3], [], 3);
 assert (v, [0, NaN, 0]);
 assert (m, [1, NaN, 3]);
***** test <*63203>
 [v, m] = std ([1, 2, 3; 3, Inf, 5]);
 assert (v, sqrt ([2, NaN, 2]));
 assert (m, [2, Inf, 4]);
***** test <*63203>
 [v, m] = std ([1, Inf, 3; 3, Inf, 5]);
 assert (v, sqrt ([2, NaN, 2]));
 assert (m, [2, Inf, 4]);
***** test <*63203>
 [v, m] = std ([1, 2, 3; 3, NaN, 5]);
 assert (v, sqrt ([2, NaN, 2]));
 assert (m, [2, NaN, 4]);
***** test <*63203>
 [v, m] = std ([1, NaN, 3; 3, NaN, 5]);
 assert (v, sqrt ([2, NaN, 2]));
 assert (m, [2, NaN, 4]);
***** test <*63203>
 [v, m] = std ([Inf, 2, NaN]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([Inf, 2, NaN]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([NaN, 2, Inf]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([NaN, 2, Inf]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([Inf, 2, NaN], [], 1);
 assert (v, [NaN, 0, NaN]);
 assert (m, [Inf, 2, NaN]);
***** test <*63203>
 [v, m] = std ([Inf, 2, NaN], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([NaN, 2, Inf], [], 1);
 assert (v, [NaN, 0, NaN]);
 assert (m, [NaN, 2, Inf]);
***** test <*63203>
 [v, m] = std ([NaN, 2, Inf], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, 3, NaN; 3, 5, Inf]);
 assert (v, sqrt ([2, 2, NaN]));
 assert (m, [2, 4, NaN]);
***** test <*63203>
 [v, m] = std ([1, 3, Inf; 3, 5, NaN]);
 assert (v, sqrt ([2, 2, NaN]));
 assert (m, [2, 4, NaN]);
***** test <*63291>
 [v, m] = std (2 * eye (2));
 assert (v, sqrt ([2, 2]));
 assert (m, [1, 1]);
***** test <*63291>
 [v, m] = std (4 * eye (2), [1, 3]);
 assert (v, sqrt ([3, 3]));
 assert (m, [1, 3]);
***** test <*63291>
 [v, m] = std (sparse (2 * eye (2)));
 assert (full (v), sqrt ([2, 2]));
 assert (full (m), [1, 1]);
***** test <*63291>
 [v, m] = std (sparse (4 * eye (2)), [1, 3]);
 assert (full (v), sqrt ([3, 3]));
 assert (full (m), [1, 3]);
***** test <*63291>
 [v, m] = std (sparse (eye (2)));
 assert (issparse (v));
 assert (issparse (m));
***** test <*63291>
 [v, m] = std (sparse (eye (2)), [1, 3]);
 assert (issparse (v));
 assert (issparse (m));
***** error <Invalid call> std ()
***** error <Invalid call> std (1, 2, "omitnan", 3)
***** error <Invalid call> std (1, 2, 3, 4)
***** error <Invalid call> std (1, 2, 3, 4, 5)
***** error <Invalid call> std (1, "foo")
***** error <Invalid call> std (1, [], "foo")
***** error <normalization scalar must be either 0 or 1> std ([1 2 3], 2)
***** error <normalization scalar must be either 0 or 1> std ([1 2], 2, "all")
***** error <normalization scalar must be either 0 or 1> std ([1 2],0.5, "all")
***** error <weights must not contain any negative values> std (1, -1)
***** error <weights must not contain any negative values> std (1, [1 -1])
***** error <weights must not contain any negative values> ...
 std ([1 2 3], [1 -1 0])
***** error <X must be a numeric vector or matrix> std ({1:5})
***** error <X must be a numeric vector or matrix> std ("char")
***** error <X must be a numeric vector or matrix> std (['A'; 'B'])
***** error <DIM must be a positive integer> std (1, [], ones (2,2))
***** error <DIM must be a positive integer> std (1, 0, 1.5)
***** error <DIM must be a positive integer> std (1, [], 0)
***** error <DIM must be a positive integer> std (1, [], 1.5)
***** error <DIM must be a positive integer> std ([1 2 3], [], [-1 1])
***** error <VECDIM must contain non-repeating positive integers> ...
 std (repmat ([1:20;6:25], [5 2 6 3]), 0, [1 2 2 2])
***** error <weight matrix or array does not match X in size> ...
 std ([1 2], eye (2))
***** error <weight matrix or array does not match X in size> ...
 std ([1 2 3 4], [1 2; 3 4])
***** error <weight matrix or array does not match X in size> ...
 std ([1 2 3 4], [1 2; 3 4], 1)
***** error <weight matrix or array does not match X in size> ...
 std ([1 2 3 4], [1 2; 3 4], [2 3])
***** error <weight matrix or array does not match X in size> ...
 std (ones (2, 2), [1 2], [1 2])
***** error <weight matrix or array does not match X in size> ...
 std ([1 2 3 4; 5 6 7 8], [1 2 1 2 1; 1 2 1 2 1], 1)
***** error <weight matrix or array does not match X in size> ...
 std (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), [2 3])
***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3 4])
***** error <weight vector length does not match> std ([1 2], [1 2 3])
***** error <weight vector length does not match> std (1, [1 2])
***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3 4], 1)
***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3], 2)
***** error <weight vector length does not match> std ([1 2], [1 2], 1)
***** error <'all' flag cannot be used with DIM or VECDIM options> ...
 std (1, [], 1, "all")
***** error <weight vector element count does not match X> ...
 std ([1 2 3; 2 3 4], [1 3], "all")
***** error <weight matrix or array does not match X in size> ...
 std (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), "all")
162 tests, 162 passed, 0 known failure, 0 skipped
[inst/shadow9/median.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/shadow9/median.m
***** assert (median (1), 1)
***** assert (median ([1,2,3]), 2)
***** assert (median ([1,2,3]'), 2)
***** assert (median (cat(3,3,1,2)), 2)
***** assert (median ([3,1,2]), 2)
***** assert (median ([2,4,6,8]), 5)
***** assert (median ([8,2,6,4]), 5)
***** assert (median (single ([1,2,3])), single (2))
***** assert (median ([1,2], 3), [1,2])
***** test
 x = [1, 2, 3, 4, 5, 6];
 x2 = x';
 y = [1, 2, 3, 4, 5, 6, 7];
 y2 = y';

 assert (median (x) == median (x2) && median (x) == 3.5);
 assert (median (y) == median (y2) && median (y) == 4);
 assert (median ([x2, 2 * x2]), [3.5, 7]);
 assert (median ([y2, 3 * y2]), [4, 12]);
***** test
 in = [1 2 3];
 out = 2;
 assert (median (in, "default"), median (in));
 assert (median (in, "default"), out);
***** test
 in = single ([1 2 3]);
 out = 2;
 assert (median (in, "default"), single (median (in)));
 assert (median (in, "default"), single (out));
 assert (median (in, "double"), double (out));
 assert (median (in, "native"), single (out));
***** test
 in = uint8 ([1 2 3]);
 out = 2;
 assert (median (in, "default"), double (median (in)));
 assert (median (in, "default"), double (out));
 assert (median (in, "double"), out);
 assert (median (in, "native"), uint8 (out));
***** test
 in = logical ([1 0 1]);
 out = 1;
 assert (median (in, "default"), double (median (in)));
 assert (median (in, "default"), double (out));
 assert (median (in, "double"), double (out));
 assert (median (in, "native"), double (out));
***** test
 x = repmat ([2 2.1 2.2 2 NaN; 3 1 2 NaN 5; 1 1.1 1.4 5 3], [1, 1, 4]);
 y = repmat ([2 1.1 2 NaN NaN], [1, 1, 4]);
 assert (median (x), y);
 assert (median (x, 1), y);
 y = repmat ([2 1.1 2 3.5 4], [1, 1, 4]);
 assert (median (x, "omitnan"), y);
 assert (median (x, 1, "omitnan"), y);
 y = repmat ([2.05; 2.5; 1.4], [1, 1, 4]);
 assert (median (x, 2, "omitnan"), y);
 y = repmat ([NaN; NaN; 1.4], [1, 1, 4]);
 assert (median (x, 2), y);
 assert (median (x, "all"), NaN);
 assert (median (x, "all", "omitnan"), 2);
***** assert (median (cat (3, 3, 1, NaN, 2), "omitnan"), 2)
***** assert (median (cat (3, 3, 1, NaN, 2), 3, "omitnan"), 2)
***** test
 assert (median (true, "all"), logical (1));
 assert (median (false), logical (0));
 assert (median ([true false true]), true);
 assert (median ([true false true], 2), true);
 assert (median ([true false true], 1), logical ([1 0 1]));
 assert (median ([true false NaN], 1), [1 0 NaN]);
 assert (median ([true false NaN], 2), NaN);
 assert (median ([true false NaN], 2, "omitnan"), 0.5);
 assert (median ([true false NaN], 2, "omitnan", "native"), double(0.5));
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (median (x, [3 2])), [10 1 1 3]);
 assert (size (median (x, [1 2])), [1 1 6 3]);
 assert (size (median (x, [1 2 4])), [1 1 6]);
 assert (size (median (x, [1 4 3])), [1 40]);
 assert (size (median (x, [1 2 3 4])), [1 1]);
***** assert (median (ones (2,2), 3), ones (2,2))
***** assert (median (ones (2,2,2), 99), ones (2,2,2))
***** assert (median (magic (3), 3), magic (3))
***** assert (median (magic (3), [1 3]), [4, 5, 6])
***** assert (median (magic (3), [1 99]), [4, 5, 6])
***** test
 x = repmat ([2 2.1 2.2 2 NaN; 3 1 2 NaN 5; 1 1.1 1.4 5 3], [1, 1, 4]);
 assert (median (x, [3 2]), [NaN NaN 1.4]');
 assert (median (x, [3 2], "omitnan"), [2.05 2.5 1.4]');
 assert (median (x, [1 3]), [2 1.1 2 NaN NaN]);
 assert (median (x, [1 3], "omitnan"), [2 1.1 2 3.5 4]);
***** assert (median (NaN), NaN)
***** assert (median (NaN, "omitnan"), NaN)
***** assert (median (NaN (2)), [NaN NaN])
***** assert (median (NaN (2), "omitnan"), [NaN NaN])
***** assert (median ([1 NaN 3]), NaN)
***** assert (median ([1 NaN 3], 1), [1 NaN 3])
***** assert (median ([1 NaN 3], 2), NaN)
***** assert (median ([1 NaN 3]'), NaN)
***** assert (median ([1 NaN 3]', 1), NaN)
***** assert (median ([1 NaN 3]', 2), [1; NaN; 3])
***** assert (median ([1 NaN 3], "omitnan"), 2)
***** assert (median ([1 NaN 3]', "omitnan"), 2)
***** assert (median ([1 NaN 3], 1, "omitnan"), [1 NaN 3])
***** assert (median ([1 NaN 3], 2, "omitnan"), 2)
***** assert (median ([1 NaN 3]', 1, "omitnan"), 2)
***** assert (median ([1 NaN 3]', 2, "omitnan"), [1; NaN; 3])
***** assert (median ([1 2 NaN 3]), NaN)
***** assert (median ([1 2 NaN 3], "omitnan"), 2)
***** assert (median ([1,2,NaN;4,5,6;NaN,8,9]), [NaN, 5, NaN])
***** assert <*64011> (median ([1,2,NaN;4,5,6;NaN,8,9], "omitnan"), [2.5, 5, 7.5], eps)
***** assert (median ([1 2 ; NaN 4]), [NaN 3])
***** assert (median ([1 2 ; NaN 4], "omitnan"), [1 3])
***** assert (median ([1 2 ; NaN 4], 1, "omitnan"), [1 3])
***** assert (median ([1 2 ; NaN 4], 2, "omitnan"), [1.5; 4], eps)
***** assert (median ([1 2 ; NaN 4], 3, "omitnan"), [1 2 ; NaN 4])
***** assert (median ([NaN 2 ; NaN 4]), [NaN 3])
***** assert (median ([NaN 2 ; NaN 4], "omitnan"), [NaN 3])
***** assert (median (ones (1, 0, 3)), NaN (1, 1, 3))
***** assert <*65405> (median ([NaN NaN], 1, "omitnan"), [NaN NaN])
***** assert <*65405> (median ([NaN NaN], 2, "omitnan"), NaN)
***** assert <*65405> (median ([NaN NaN]', 1, "omitnan"), NaN)
***** assert <*65405> (median ([NaN NaN]', 2, "omitnan"), [NaN; NaN])
***** assert <*65405> (median ([NaN NaN], "omitnan"), NaN)
***** assert <*65405> (median ([NaN NaN]', "omitnan"), NaN)
***** assert <*65405> (median (NaN(1,9), 1, "omitnan"), NaN(1,9))
***** assert <*65405> (median (NaN(1,9), 2, "omitnan"), NaN)
***** assert <*65405> (median (NaN(1,9), 3, "omitnan"), NaN(1,9))
***** assert <*65405> (median (NaN(9,1), 1, "omitnan"), NaN)
***** assert <*65405> (median (NaN(9,1), 2, "omitnan"), NaN(9,1))
***** assert <*65405> (median (NaN(9,1), 3, "omitnan"), NaN(9,1))
***** assert <*65405> (median (NaN(9,2), 1, "omitnan"), NaN(1,2))
***** assert <*65405> (median (NaN(9,2), 2, "omitnan"), NaN(9,1))
***** assert <*65405> (median (NaN(9,2), "omitnan"), NaN(1,2))
***** assert (median (NaN("single")), NaN("single"))
***** assert (median (NaN("single"), "omitnan"), NaN("single"))
***** assert (median (NaN("single"), "double"), NaN("double"))
***** assert (median (single([1 2 ; NaN 4])), single([NaN 3]))
***** assert (median (single([1 2 ; NaN 4]), "double"), double([NaN 3]))
***** assert (median (single([1 2 ; NaN 4]), "omitnan"), single([1 3]))
***** assert (median (single([1 2 ; NaN 4]), "omitnan", "double"), double([1 3]))
***** assert (median (single([NaN 2 ; NaN 4]), "double"), double([NaN 3]))
***** assert (median (single([NaN 2 ; NaN 4]), "omitnan"), single([NaN 3]))
***** assert (median (single([NaN 2 ; NaN 4]), "omitnan", "double"), double([NaN 3]))
***** test <*64011>
 x = [magic(3), magic(3)];
 x([3, 7, 11, 12, 16, 17]) = NaN;
 ynan = [NaN, 5, NaN, NaN, 5, NaN];
 yomitnan = [5.5, 5, 4.5, 8, 5, 2];
 assert (median (x), ynan);
 assert (median (x, "omitnan"), yomitnan, eps);
 assert (median (cat (3, x, x)), cat (3, ynan, ynan));
 assert (median (cat (3, x, x), "omitnan"), cat (3, yomitnan, yomitnan), eps);
***** assert (median (Inf), Inf)
***** assert (median (-Inf), -Inf)
***** assert (median ([-Inf Inf]), NaN)
***** assert (median ([3 Inf]), Inf)
***** assert (median ([3 4 Inf]), 4)
***** assert (median ([Inf 3 4]), 4)
***** assert (median ([Inf 3 Inf]), Inf)
***** assert (median ([1, 2, Inf]), 2)
***** assert (median ([1, 2, Inf, Inf]), Inf)
***** assert (median ([1, -Inf, Inf, Inf]), Inf)
***** assert (median ([-Inf, -Inf, Inf, Inf]), NaN)
***** assert (median([-Inf, Inf, Inf, Inf]), Inf)
***** assert (median([-Inf, -Inf, -Inf, Inf]), -Inf)
***** assert (median([-Inf, -Inf, -Inf, 2]), -Inf)
***** assert (median([-Inf, -Inf, 1, 2]), -Inf)
***** assert (median ([]), NaN)
***** assert (median (ones(1,0)), NaN)
***** assert (median (ones(0,1)), NaN)
***** assert (median ([], 1), NaN(1,0))
***** assert (median ([], 2), NaN(0,1))
***** assert (median ([], 3), NaN(0,0))
***** assert (median (ones(1,0), 1), NaN(1,0))
***** assert (median (ones(1,0), 2), NaN(1,1))
***** assert (median (ones(1,0), 3), NaN(1,0))
***** assert (median (ones(0,1), 1), NaN(1,1))
***** assert (median (ones(0,1), 2), NaN(0,1))
***** assert (median (ones(0,1), 3), NaN(0,1))
***** assert (median (ones(0,1,0,1), 1), NaN(1,1,0))
***** assert (median (ones(0,1,0,1), 2), NaN(0,1,0))
***** assert (median (ones(0,1,0,1), 3), NaN(0,1,1))
***** assert (median (ones(0,1,0,1), 4), NaN(0,1,0))
***** assert (median([1 3 3i 2 1i]), 2)
***** assert (median([1 2 4i; 3 2i 4]), [2, 1+1i, 2+2i])
***** shared a, b, x, y
 old_state = rand ("state");
 restore_state = onCleanup (@() rand ("state", old_state));
 rand ("state", 2);
 a = rand (2,3,4,5);
 b = rand (3,4,6,5);
 x = sort (a, 4);
 y = sort (b, 3);
***** assert <*35679> (median (a, 4), x(:, :, :, 3))
***** assert <*35679> (median (b, 3), (y(:, :, 3, :) + y(:, :, 4, :))/2)
***** shared   ## Clear shared to prevent variable echo for any later test failures
***** test
 x = ones(15,1,4);
 x([13,15],1,:) = NaN;
 assert (median (x, 1, "omitnan"), ones (1,1,4))
***** assert (median ([true, false]), true)
***** assert (median (logical ([])), false)
***** assert (median (uint8 ([1, 3])), uint8 (2))
***** assert (median (uint8 ([])), uint8 (NaN))
***** assert (median (uint8 ([NaN 10])), uint8 (5))
***** assert (median (int8 ([1, 3, 4])), int8 (3))
***** assert (median (int8 ([])), int8 (NaN))
***** assert (median (single ([1, 3, 4])), single (3))
***** assert (median (single ([1, 3, NaN])), single (NaN))
***** assert <54567> (median (uint8 ([253, 255])), uint8 (254))
***** assert <54567> (median (uint8 ([253, 254])), uint8 (254))
***** assert <54567> (median (int8 ([127, 126, 125, 124; 1 3 5 9])), ...
                  int8 ([64 65 65 67]))
***** assert <54567> (median (int8 ([127, 126, 125, 124; 1 3 5 9]), 2), ...
                  int8 ([126; 4]))
***** assert <54567> (median (int64 ([intmax("int64"), intmax("int64")-2])), ...
                  intmax ("int64") - 1)
***** assert <54567> (median ( ...
                 int64 ([intmax("int64"), intmax("int64")-2; 1 2]), 2), ...
                 int64([intmax("int64") - 1; 2]))
***** assert <54567> (median (uint64 ([intmax("uint64"), intmax("uint64")-2])), ...
                  intmax ("uint64") - 1)
***** assert <54567> (median ( ...
                 uint64 ([intmax("uint64"), intmax("uint64")-2; 1 2]), 2), ...
                 uint64([intmax("uint64") - 1; 2]))
***** assert <54567> (median (...
 [intmin('int8') intmin('int8')+5 intmax('int8')-5 intmax('int8')]), ...
 int8(-1))
***** assert <54567> (median ([int8([1 2 3 4]); ...
 intmin('int8') intmin('int8')+5 intmax('int8')-5 intmax('int8')], 2), ...
 int8([3;-1]))
***** assert <54567> (median (...
 [intmin('int64') intmin('int64')+5 intmax('int64')-5 intmax('int64')]), ...
 int64(-1))
***** assert <54567> (median ([int64([1 2 3 4]); ...
 intmin('int64') intmin('int64')+5 intmax('int64')-5 intmax('int64')], 2), ...
 int64([3;-1]))
***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2]), ...
  intmax("uint64")-1)
***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "default"), ...
  double(intmax("uint64")-1))
***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "double"), ...
  double(intmax("uint64")-1))
***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "native"), ...
  intmax("uint64")-1)
***** assert (median ([1 2 3], "aLL"), 2)
***** assert (median ([1 2 3], "OmitNan"), 2)
***** assert (median ([1 2 3], "DOUBle"), 2)
***** error <Invalid call> median ()
***** error <Invalid call> median (1, 2, 3)
***** error <Invalid call> median (1, 2, 3, 4)
***** error <Invalid call> median (1, "all", 3)
***** error <Invalid call> median (1, "b")
***** error <Invalid call> median (1, 1, "foo")
***** error <'all' cannot be used with> median (1, 3, "all")
***** error <'all' cannot be used with> median (1, [2 3], "all")
***** error <X must be either numeric or logical> median ({1:5})
***** error <X must be either numeric or logical> median ("char")
***** error <only one OUTTYPE can be specified> median(1, "double", "native")
***** error <DIM must be a positive integer> median (1, ones (2,2))
***** error <DIM must be a positive integer> median (1, 1.5)
***** error <DIM must be a positive integer> median (1, 0)
***** error <DIM must be a positive integer> median ([1 2 3], [-1 1])
***** error <VECDIM must contain non-repeating> median(1, [1 2 2])
159 tests, 159 passed, 0 known failure, 0 skipped
[inst/shadow9/var.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/shadow9/var.m
***** assert (var (13), 0)
***** assert (var (single (13)), single (0))
***** assert (var ([1,2,3]), 1)
***** assert (var ([1,2,3], 1), 2/3, eps)
***** assert (var ([1,2,3], [], 1), [0,0,0])
***** assert (var ([1,2,3], [], 3), [0,0,0])
***** assert (var (5, 99), 0)
***** assert (var (5, 99, 1), 0)
***** assert (var (5, 99, 2), 0)
***** assert (var ([5 3], [99 99], 2), 1)
***** assert (var ([1:7], [1:7]), 3)
***** assert (var ([eye(3)], [1:3]), [5/36, 2/9, 1/4], eps)
***** assert (var (ones (2,2,2), [1:2], 3), [(zeros (2,2))])
***** assert (var ([1 2; 3 4], 0, 'all'), var ([1:4]))
***** assert (var (reshape ([1:8], 2, 2, 2), 0, [1 3]), [17/3 17/3], eps)
***** assert (var ([1 2 3;1 2 3], [], [1 2]), 0.8, eps)
***** test
 x = [-10:10];
 y = [x;x+5;x-5];
 assert (var (x), 38.5);
 assert (var (y, [], 2), [38.5; 38.5; 38.5]);
 assert (var (y, 0, 2), [38.5; 38.5; 38.5]);
 assert (var (y, 1, 2), ones (3,1) * 36.66666666666666, 1e-14);
 assert (var (y, "all"), 54.19354838709678, 1e-14);
 y(2,4) = NaN;
 assert (var (y, "all"), NaN);
 assert (var (y, "all", "includenan"), NaN);
 assert (var (y, "all", "omitnan"), 55.01533580116342, 1e-14);
 assert (var (y, 0, 2, "includenan"), [38.5; NaN; 38.5]);
 assert (var (y, [], 2), [38.5; NaN; 38.5]);
 assert (var (y, [], 2, "omitnan"), [38.5; 37.81842105263158; 38.5], 1e-14);
***** assert (var ([1 NaN 3], [1 2 3], "omitnan"), 0.75, eps)
***** assert (var ([1 2 3], [1 NaN 3], "omitnan"), 0.75, eps)
***** assert (var (magic(3), [1 NaN 3], "omitnan"), [3 12 3], eps)
***** assert (var ([1 NaN 3], [1 2 3], "omitnan", "all"), 0.75, eps)
***** assert (var ([1 NaN 3], [1 2 3], "all", "omitnan"), 0.75, eps)
***** assert (var ([1 2 3], [1 NaN 3], "omitnan", "all"), 0.75, eps)
***** assert (var ([1 NaN 3], [1 2 3], 2, "omitnan"), 0.75, eps)
***** assert (var ([1 2 3], [1 NaN 3], 2, "omitnan"), 0.75, eps)
***** assert (var (magic(3), [1 NaN 3], 1, "omitnan"), [3 12 3], eps)
***** assert (var (magic(3), [1 NaN 3], 2, "omitnan"), [0.75;3;0.75], eps)
***** assert (var ([4 4; 4 6; 6 6], [1 3], 2, 'omitnan'), [0;0.75;0], eps)
***** assert (var ([4 NaN; 4 6; 6 6], [1 2 3], 1, 'omitnan'), [1 0])
***** assert (var ([4 NaN; 4 6; 6 6], [1 3], 2, 'omitnan'), [0;0.75;0], eps)
***** assert (var (3*reshape(1:18, [3 3 2]), [1 2 3], 1, 'omitnan'), ones(1,3,2)*5)
***** assert (var (reshape(1:18, [3 3 2]), [1 2 3], 2, 'omitnan'), 5*ones(3,1,2))
***** assert (var (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 2], 'omitnan'), ...
         60 * ones(1,1,2))
***** assert (var (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 4], 'omitnan'), ...
         6 * ones(1,3,2))
***** assert (var (6*reshape(1:18, [3 3 2]), ones (3,3,2), [1:3], 'omitnan'), 969)
***** test
 x = reshape(1:18, [3 3 2]);
 x([2, 14]) = NaN;
 w = ones (3,3,2);
 assert (var (16*x, w, [1:3], 'omitnan'), 6519);
***** test
 x = reshape(1:18, [3 3 2]);
 w = ones (3,3,2);
 w([2, 14]) = NaN;
 assert (var (16*x, w, [1:3], 'omitnan'), 6519);
***** assert (var ([1 2 3], "aLl"), 1);
***** assert (var ([1 2 3], "OmitNan"), 1);
***** assert (var ([1 2 3], "IncludeNan"), 1);
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 assert (size (var (x, 0, [3 2])), [10, 1, 1, 3]);
 assert (size (var (x, 1, [1 2])), [1, 1, 6, 3]);
 assert (size (var (x, [], [1 2 4])), [1, 1, 6]);
 assert (size (var (x, 0, [1 4 3])), [1, 40]);
 assert (size (var (x, [], [1 2 3 4])), [1, 1]);
***** assert (var (3*magic(3)), [63 144 63])
***** assert (var (3*magic(3), 'omitnan'), [63 144 63])
***** assert (var (3*magic(3), 1), [42 96 42])
***** assert (var (3*magic(3), 1, 'omitnan'), [42 96 42])
***** assert (var (3*magic(3), ones(1,3), 1), [42 96 42])
***** assert (var (3*magic(3), ones(1,3), 1, 'omitnan'), [42 96 42])
***** assert (var (2*magic(3), [1 1 NaN], 1, 'omitnan'), [25 16 1])
***** assert (var (3*magic(3), ones(3,3)), [42 96 42])
***** assert (var (3*magic(3), ones(3,3), 'omitnan'), [42 96 42])
***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 'omitnan'), [42 36 42])
***** assert (var (3*magic(3), ones(3,3), 1), [42 96 42])
***** assert (var (3*magic(3), ones(3,3), 1, 'omitnan'), [42 96 42])
***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 1, 'omitnan'), [42 36 42])
***** assert (var (3*magic(3), ones(3,3), [1 4]), [42 96 42])
***** assert (var (3*magic(3), ones(3,3), [1 4], 'omitnan'), [42 96 42])
***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1],[1 4],'omitnan'), [42 36 42])
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 v = repmat (33.38912133891213, [10, 1, 1, 3]);
 assert (var (x, 0, [3, 2]), v, 1e-14);
 v = repmat (33.250, [10, 1, 1, 3]);
 assert (var (x, 1, [3, 2]), v, 1e-14);
 x(2,5,6,3) = NaN;
 v(2,1,1,3) = NaN;
 assert (var (x, 1, [3, 2]), v, 4e-14);
 v = repmat (33.38912133891213, [10 1 1 3]);
 v(2,1,1,3) = NaN;
 assert (var (x, [], [3, 2]), v, 4e-14);
 v(2,1,1,3) = 33.40177912169048;
 assert (var (x, [], [3, 2], "omitnan"), v, 4e-14);
***** assert (var (ones (2,2,2), [1:2], 3), [(zeros (2, 2))])
***** assert (var (magic (3), [1:9], "all"), 6.666666666666667, 1e-14)
***** assert (var (ones (2,2), [], 3), zeros (2,2))
***** assert (var (ones (2,2,2), [], 99), zeros (2,2,2))
***** assert (var (magic (3), [], 3), zeros (3,3))
***** assert (var (magic (3), [], 1), [7, 16, 7])
***** assert (var (magic (3), [], [1 3]), [7, 16, 7])
***** assert (var (magic (3), [], [1 99]), [7, 16, 7])
***** assert (var ([]), NaN)
***** assert (class (var (single ([]))), "single")
***** assert (var ([],[],1), NaN(1,0))
***** assert (var ([],[],2), NaN(0,1))
***** assert (var ([],[],3), [])
***** assert (class (var (single ([]), [], 1)), "single")
***** assert (var (ones (1,0)), NaN)
***** assert (var (ones (1,0), [], 1), NaN(1,0))
***** assert (var (ones (1,0), [], 2), NaN)
***** assert (var (ones (1,0), [], 3), NaN(1,0))
***** assert (class (var (ones (1, 0, "single"), [], 1)), "single")
***** assert (var (ones (0,1)), NaN)
***** assert (var (ones (0,1), [], 1), NaN)
***** assert (var (ones (0,1), [], 2), NaN(0,1))
***** assert (var (ones (0,1), [], 3), NaN(0,1))
***** assert (var (ones (1,3,0,2)), NaN(1,1,0,2))
***** assert (var (ones (1,3,0,2), [], 1), NaN(1,3,0,2))
***** assert (var (ones (1,3,0,2), [], 2), NaN(1,1,0,2))
***** assert (var (ones (1,3,0,2), [], 3), NaN(1,3,1,2))
***** assert (var (ones (1,3,0,2), [], 4), NaN(1,3,0))
***** test
 [~, m] = var ([]);
 assert (m, NaN);
***** test <*62395>
 [~, m] = var (13);
 assert (m, 13);
 [~, m] = var (single(13));
 assert (m, single(13));
 [~, m] = var ([1, 2, 3; 3 2 1], []);
 assert (m, [2 2 2]);
 [~, m] = var ([1, 2, 3; 3 2 1], [], 1);
 assert (m, [2 2 2]);
 [~, m] = var ([1, 2, 3; 3 2 1], [], 2);
 assert (m, [2 2]');
 [~, m] = var ([1, 2, 3; 3 2 1], [], 3);
 assert (m, [1 2 3; 3 2 1]);
***** test <*62395>
 [~, m] = var (5,99);
 assert (m, 5);
 [~, m] = var ([1:7], [1:7]);
 assert (m, 5);
 [~, m] = var ([eye(3)], [1:3]);
 assert (m, [1/6, 1/3, 0.5], eps);
 [~, m] = var (ones (2,2,2), [1:2], 3);
 assert (m, ones (2,2));
 [~, m] = var ([1 2; 3 4], 0, 'all');
 assert (m, 2.5, eps);
 [~, m] = var (reshape ([1:8], 2, 2, 2), 0, [1 3]);
 assert (m, [3.5, 5.5], eps);
***** test
 [v, m] = var (4 * eye (2), [1, 3]);
 assert (v, [3, 3]);
 assert (m, [1, 3]);
***** test <*62395>
 [~, m] = var ([]);
 assert (m, NaN);
***** test <*62395>
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 [~, m] = var (x, 0, [3 2]);
 assert (m, mean (x, [3 2]));
 [~, m] = var (x, 0, [1 2]);
 assert (m, mean (x, [1 2]));
 [~, m] = var (x, 0, [1 3 4]);
 assert (m, mean (x, [1 3 4]));
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 x(2,5,6,3) = NaN;
 [~, m] = var (x, 0, [3 2], "omitnan");
 assert (m, mean (x, [3 2], "omitnan"));
***** test <*63203>
 [v, m] = var (Inf);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = var (NaN);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, Inf, 3]);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = var ([1, Inf, 3]');
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = var ([1, NaN, 3]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, NaN, 3]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, Inf, 3], [], 1);
 assert (v, [0, NaN, 0]);
 assert (m, [1, Inf, 3]);
***** test <*63203>
 [v, m] = var ([1, Inf, 3], [], 2);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = var ([1, Inf, 3], [], 3);
 assert (v, [0, NaN, 0]);
 assert (m, [1, Inf, 3]);
***** test <*63203>
 [v, m] = var ([1, NaN, 3], [], 1);
 assert (v, [0, NaN, 0]);
 assert (m, [1, NaN, 3]);
***** test <*63203>
 [v, m] = var ([1, NaN, 3], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, NaN, 3], [], 3);
 assert (v, [0, NaN, 0]);
 assert (m, [1, NaN, 3]);
***** test <*63203>
 [v, m] = var ([1, 2, 3; 3, Inf, 5]);
 assert (v, [2, NaN, 2]);
 assert (m, [2, Inf, 4]);
***** test <*63203>
 [v, m] = var ([1, Inf, 3; 3, Inf, 5]);
 assert (v, [2, NaN, 2]);
 assert (m, [2, Inf, 4]);
***** test <*63203>
 [v, m] = var ([1, 2, 3; 3, NaN, 5]);
 assert (v, [2, NaN, 2]);
 assert (m, [2, NaN, 4]);
***** test <*63203>
 [v, m] = var ([1, NaN, 3; 3, NaN, 5]);
 assert (v, [2, NaN, 2]);
 assert (m, [2, NaN, 4]);
***** test <*63203>
 [v, m] = var ([Inf, 2, NaN]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([Inf, 2, NaN]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([NaN, 2, Inf]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([NaN, 2, Inf]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([Inf, 2, NaN], [], 1);
 assert (v, [NaN, 0, NaN]);
 assert (m, [Inf, 2, NaN]);
***** test <*63203>
 [v, m] = var ([Inf, 2, NaN], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([NaN, 2, Inf], [], 1);
 assert (v, [NaN, 0, NaN]);
 assert (m, [NaN, 2, Inf]);
***** test <*63203>
 [v, m] = var ([NaN, 2, Inf], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, 3, NaN; 3, 5, Inf]);
 assert (v, [2, 2, NaN]);
 assert (m, [2, 4, NaN]);
***** test <*63203>
 [v, m] = var ([1, 3, Inf; 3, 5, NaN]);
 assert (v, [2, 2, NaN]);
 assert (m, [2, 4, NaN]);
***** test <*63291>
 [v, m] = var (2 * eye (2));
 assert (v, [2, 2]);
 assert (m, [1, 1]);
***** test <*63291>
 [v, m] = var (4 * eye (2), [1, 3]);
 assert (v, [3, 3]);
 assert (m, [1, 3]);
***** test <*63291>
 [v, m] = var (sparse (2 * eye (2)));
 assert (full (v), [2, 2]);
 assert (full (m), [1, 1]);
***** test <*63291>
 [v, m] = var (sparse (4 * eye (2)), [1, 3]);
 assert (full (v), [3, 3]);
 assert (full (m), [1, 3]);
***** test<*63291>
 [v, m] = var (sparse (eye (2)));
 assert (issparse (v));
 assert (issparse (m));
***** test<*63291>
 [v, m] = var (sparse (eye (2)), [1, 3]);
 assert (issparse (v));
 assert (issparse (m));
***** error <Invalid call> var ()
***** error <Invalid call> var (1, 2, "omitnan", 3)
***** error <Invalid call> var (1, 2, 3, 4)
***** error <Invalid call> var (1, 2, 3, 4, 5)
***** error <Invalid call> var (1, "foo")
***** error <Invalid call> var (1, [], "foo")
***** error <normalization scalar must be either 0 or 1> var ([1 2 3], 2)
***** error <normalization scalar must be either 0 or 1> var ([1 2], 2, "all")
***** error <normalization scalar must be either 0 or 1> var ([1 2],0.5, "all")
***** error <weights must not contain any negative values> var (1, -1)
***** error <weights must not contain any negative values> var (1, [1 -1])
***** error <weights must not contain any negative values> ...
 var ([1 2 3], [1 -1 0])
***** error <X must be a numeric vector or matrix> var ({1:5})
***** error <X must be a numeric vector or matrix> var ("char")
***** error <X must be a numeric vector or matrix> var (['A'; 'B'])
***** error <DIM must be a positive integer> var (1, [], ones (2,2))
***** error <DIM must be a positive integer> var (1, 0, 1.5)
***** error <DIM must be a positive integer> var (1, [], 0)
***** error <DIM must be a positive integer> var (1, [], 1.5)
***** error <DIM must be a positive integer> var ([1 2 3], [], [-1 1])
***** error <VECDIM must contain non-repeating positive integers> ...
 var (repmat ([1:20;6:25], [5 2 6 3]), 0, [1 2 2 2])
***** error <weight matrix or array does not match X in size> ...
 var ([1 2], eye (2))
***** error <weight matrix or array does not match X in size> ...
 var ([1 2 3 4], [1 2; 3 4])
***** error <weight matrix or array does not match X in size> ...
 var ([1 2 3 4], [1 2; 3 4], 1)
***** error <weight matrix or array does not match X in size> ...
 var ([1 2 3 4], [1 2; 3 4], [2 3])
***** error <weight matrix or array does not match X in size> ...
 var (ones (2, 2), [1 2], [1 2])
***** error <weight matrix or array does not match X in size> ...
 var ([1 2 3 4; 5 6 7 8], [1 2 1 2 1; 1 2 1 2 1], 1)
***** error <weight matrix or array does not match X in size> ...
 var (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), [2 3])
***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3 4])
***** error <weight vector length does not match> var ([1 2], [1 2 3])
***** error <weight vector length does not match> var (1, [1 2])
***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3 4], 1)
***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3], 2)
***** error <weight vector length does not match> var ([1 2], [1 2], 1)
***** error <'all' flag cannot be used with DIM or VECDIM options> ...
 var (1, [], 1, "all")
***** error <weight vector element count does not match X> ...
 var ([1 2 3; 2 3 4], [1 3], "all")
***** error <weight matrix or array does not match X in size> ...
 var (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), "all")
162 tests, 162 passed, 0 known failure, 0 skipped
[inst/shadow9/mean.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/shadow9/mean.m
***** test
 x = -10:10;
 y = x';
 z = [y, y+10];
 assert (mean (x), 0);
 assert (mean (y), 0);
 assert (mean (z), [0, 10]);
***** assert (mean (magic (3), 1), [5, 5, 5])
***** assert (mean (magic (3), 2), [5; 5; 5])
***** assert (mean (logical ([1 0 1 1])), 0.75)
***** assert (mean (single ([1 0 1 1])), single (0.75))
***** assert (mean ([1 2], 3), [1 2])
***** test
 in = [1 2 3];
 out = 2;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), out);
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), out);
***** test
 in = single ([1 2 3]);
 out = 2;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), single (out));
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), single (out));
***** test
 in = logical ([1 0 1]);
 out = 2/3;
 assert (mean (in, "default"), mean (in), eps);
 assert (mean (in, "default"), out, eps);
 assert (mean (in, "double"), out, eps);
 assert (mean (in, "native"), out, eps);
***** test
 in = char ("ab");
 out = 97.5;
 assert (mean (in, "default"), mean (in), eps);
 assert (mean (in, "default"), out, eps);
 assert (mean (in, "double"), out, eps);
***** test
 in = uint8 ([1 2 3]);
 out = 2;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), out);
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), uint8 (out));
***** test
 in = uint8 ([0 1 2 3]);
 out = 1.5;
 out_u8 = 2;
 assert (mean (in, "default"), mean (in), eps);
 assert (mean (in, "default"), out, eps);
 assert (mean (in, "double"), out, eps);
 assert (mean (in, "native"), uint8 (out_u8));
 assert (class (mean (in, "native")), "uint8");
***** test # internal sum exceeding intmax
 in = uint8 ([3 141 141 255]);
 out = 135;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), out);
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), uint8 (out));
 assert (class (mean (in, "native")), "uint8");
***** test # fractional answer with internal sum exceeding intmax
 in = uint8 ([1 141 141 255]);
 out = 134.5;
 out_u8 = 135;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), out);
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), uint8 (out_u8));
 assert (class (mean (in, "native")), "uint8");
***** test <54567> # large int64 sum exceeding intmax and double precision limit
 in_same = uint64 ([intmax("uint64") intmax("uint64")-2]);
 out_same = intmax ("uint64")-1;
 in_opp = int64 ([intmin("int64"), intmax("int64")-1]);
 out_opp = -1;
 in_neg = int64 ([intmin("int64") intmin("int64")+2]);
 out_neg = intmin ("int64")+1;

 ## both positive
 assert (mean (in_same, "default"), mean (in_same));
 assert (mean (in_same, "default"), double (out_same));
 assert (mean (in_same, "double"), double (out_same));
 assert (mean (in_same, "native"), uint64 (out_same));
 assert (class (mean (in_same, "native")), "uint64");

 ## opposite signs
 assert (mean (in_opp, "default"), mean (in_opp));
 assert (mean (in_opp, "default"), double (out_opp));
 assert (mean (in_opp, "double"), double (out_opp));
 assert (mean (in_opp, "native"), int64 (out_opp));
 assert (class (mean (in_opp, "native")), "int64");

 ## both negative
 assert (mean (in_neg, "default"), mean (in_neg));
 assert (mean (in_neg, "default"), double(out_neg));
 assert (mean (in_neg, "double"), double(out_neg));
 assert (mean (in_neg, "native"), int64(out_neg));
 assert (class (mean (in_neg, "native")), "int64");
***** test <54567>
 in = [(intmin('int64')+5), (intmax('int64'))-5];
 assert (mean (in, "native"), int64(-1));
 assert (class (mean (in, "native")), "int64");
 assert (mean (double(in)), double(0) );
 assert (mean (in), double(-0.5) );
 assert (mean (in, "default"), double(-0.5) );
 assert (mean (in, "double"), double(-0.5) );
 assert (mean (in, "all", "native"), int64(-1));
 assert (mean (in, 2, "native"), int64(-1));
 assert (mean (in, [1 2], "native"), int64(-1));
 assert (mean (in, [2 3], "native"), int64(-1));
 assert (mean ([intmin("int64"), in, intmax("int64")]), double(-0.5))
 assert (mean ([in; int64([1 3])], 2, "native"), int64([-1; 2]));
***** test
 x = [-10:10];
 y = [x;x+5;x-5];
 assert (mean (x), 0);
 assert (mean (y, 2), [0, 5, -5]');
 assert (mean (y, "all"), 0);
 y(2,4) = NaN;
 assert (mean (y', "omitnan"), [0 5.35 -5]);
 z = y + 20;
 assert (mean (z, "all"), NaN);
 assert (mean (z, "all", "includenan"), NaN);
 assert (mean (z, "all", "omitnan"), 20.03225806451613, 4e-14);
 m = [20 NaN 15];
 assert (mean (z'), m);
 assert (mean (z', "includenan"), m);
 m = [20 25.35 15];
 assert (mean (z', "omitnan"), m);
 assert (mean (z, 2, "omitnan"), m');
 assert (mean (z, 2, "native", "omitnan"), m');
 assert (mean (z, 2, "omitnan", "native"), m');
***** test
 assert (mean (true, "all"), 1);
 assert (mean (false), 0);
 assert (mean ([true false true]), 2/3, 4e-14);
 assert (mean ([true false true], 1), [1 0 1]);
 assert (mean ([true false NaN], 1), [1 0 NaN]);
 assert (mean ([true false NaN], 2), NaN);
 assert (mean ([true false NaN], 2, "omitnan"), 0.5);
 assert (mean ([true false NaN], 2, "omitnan", "native"), 0.5);
***** assert (mean ("abc"), double (98))
***** assert (mean ("ab"), double (97.5), eps)
***** assert (mean ("abc", "double"), double (98))
***** assert (mean ("abc", "default"), double (98))
***** test
 x = magic (4);
 x([2, 9:12]) = NaN;
 assert (mean (x), [NaN 8.5, NaN, 8.5], eps);
 assert (mean (x,1), [NaN 8.5, NaN, 8.5], eps);
 assert (mean (x,2), NaN(4,1), eps);
 assert (mean (x,3), x, eps);
 assert (mean (x, 'omitnan'), [29/3, 8.5, NaN, 8.5], eps);
 assert (mean (x, 1, 'omitnan'), [29/3, 8.5, NaN, 8.5], eps);
 assert (mean (x, 2, 'omitnan'), [31/3; 9.5; 28/3; 19/3], eps);
 assert (mean (x, 3, 'omitnan'), x, eps);
***** assert (mean ([]), NaN(1,1))
***** assert (mean (single([])), NaN(1,1,"single"))
***** assert (mean ([], 1), NaN(1,0))
***** assert (mean ([], 2), NaN(0,1))
***** assert (mean ([], 3), NaN(0,0))
***** assert (mean (ones(1,0)), NaN(1,1))
***** assert (mean (ones(1,0), 1), NaN(1,0))
***** assert (mean (ones(1,0), 2), NaN(1,1))
***** assert (mean (ones(1,0), 3), NaN(1,0))
***** assert (mean (ones(0,1)), NaN(1,1))
***** assert (mean (ones(0,1), 1), NaN(1,1))
***** assert (mean (ones(0,1), 2), NaN(0,1))
***** assert (mean (ones(0,1), 3), NaN(0,1))
***** assert (mean (ones(0,1,0)), NaN(1,1,0))
***** assert (mean (ones(0,1,0), 1), NaN(1,1,0))
***** assert (mean (ones(0,1,0), 2), NaN(0,1,0))
***** assert (mean (ones(0,1,0), 3), NaN(0,1,1))
***** assert (mean (ones(0,0,1,0)), NaN(1,0,1,0))
***** assert (mean (ones(0,0,1,0), 1), NaN(1,0,1,0))
***** assert (mean (ones(0,0,1,0), 2), NaN(0,1,1,0))
***** assert (mean (ones(0,0,1,0), 3), NaN(0,0,1,0))
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (mean (x, [3 2])), [10 1 1 3]);
 assert (size (mean (x, [1 2])), [1 1 6 3]);
 assert (size (mean (x, [1 2 4])), [1 1 6]);
 assert (size (mean (x, [1 4 3])), [1 40]);
 assert (size (mean (x, [1 2 3 4])), [1 1]);
***** assert (mean (ones (2,2), 3), ones (2,2))
***** assert (mean (ones (2,2,2), 99), ones (2,2,2))
***** assert (mean (magic (3), 3), magic (3))
***** assert (mean (magic (3), [1 3]), [5, 5, 5])
***** assert (mean (magic (3), [1 99]), [5, 5, 5])
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 m = repmat ([10.5;15.5], [5 1 1 3]);
 assert (mean (x, [3 2]), m, 4e-14);
 x(2,5,6,3) = NaN;
 m(2,1,1,3) = NaN;
 assert (mean (x, [3 2]), m, 4e-14);
 m(2,1,1,3) = 15.52301255230125;
 assert (mean (x, [3 2], "omitnan"), m, 4e-14);
***** assert (mean ([1 2 3], "aLL"), 2)
***** assert (mean ([1 2 3], "OmitNan"), 2)
***** assert (mean ([1 2 3], "DOUBle"), 2)
***** assert <*63848> (mean (ones (80e6, 1, "single")), 1, eps)
***** assert <*63848> (mean (ones (80e6, 1, "single"), "all"), 1, eps)
***** assert <*63848> (mean (ones (80e6, 1, "single"), 1), 1, eps)
***** assert <*63848> (mean (ones (80e6, 1, "single"), [1 2]), 1, eps)
***** assert <*63848> (mean (ones (80e6, 1, "single"), [1 3]), 1, eps)
***** assert <63848> (mean ([flintmax("double"), ones(1, 2^8-1, "double")]), ...
                               35184372088833-1/(2^8), eps(35184372088833))
!!!!! known bug: https://octave.org/testfailure/?63848
ASSERT errors for:  assert (mean ([flintmax("double"), ones(1, 2 ^ 8 - 1, "double")]),35184372088833 - 1 / (2 ^ 8),eps (35184372088833))

  Location  |  Observed  |  Expected  |  Reason
     ()      35184372088832 35184372088833   Abs err 1 exceeds tol 0.0078125 by 1
***** error <Invalid call to mean.  Correct usage is> mean ()
***** error <Invalid call to mean.  Correct usage is> mean (1, 2, 3)
***** error <Invalid call to mean.  Correct usage is> mean (1, 2, 3, 4)
***** error <Invalid call to mean.  Correct usage is> mean (1, "all", 3)
***** error <Invalid call to mean.  Correct usage is> mean (1, "b")
***** error <Invalid call to mean.  Correct usage is> mean (1, 1, "foo")
***** error <OUTTYPE 'native' cannot be used with char> mean ("abc", "native")
***** error <X must be either a numeric, boolean, or character> mean ({1:5})
***** error <DIM must be a positive integer> mean (1, ones (2,2))
***** error <DIM must be a positive integer> mean (1, 1.5)
***** error <DIM must be a positive integer> mean (1, 0)
***** error <DIM must be a positive integer> mean (1, [])
***** error <DIM must be a positive integer> mean (1, -1)
***** error <DIM must be a positive integer> mean (1, -1.5)
***** error <DIM must be a positive integer> mean (1, NaN)
***** error <DIM must be a positive integer> mean (1, Inf)
***** error <DIM must be a positive integer> mean (repmat ([1:20;6:25], [5 2]), -1)
***** error <DIM must be a positive integer> mean (repmat ([1:5;5:9], [5 2]), [1 -1])
***** error <DIM must be a positive integer> mean (1, ones(1,0))
***** error <VECDIM must contain non-repeating> mean (1, [2 2])
80 tests, 79 passed, 0 known failure, 1 skipped
[inst/randsample.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/randsample.m
***** test
 n = 20;
 k = 5;
 x = randsample(n, k);
 assert (size(x), [1 k]);
 x = randsample(n, k, true);
 assert (size(x), [1 k]);
 x = randsample(n, k, false);
 assert (size(x), [1 k]);
 x = randsample(n, k, true, ones(n, 1));
 assert (size(x), [1 k]);
 x = randsample(1:n, k);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, true);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, false);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, true, ones(n, 1));
 assert (size(x), [1 k]);
 x = randsample((1:n)', k);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, true);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, false);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, true, ones(n, 1));
 assert (size(x), [k 1]);
 n = 10;
 k = 100;
 x = randsample(n, k, true, 1:n);
 assert (size(x), [1 k]);
 x = randsample((1:n)', k, true);
 assert (size(x), [k 1]);
 x = randsample(k, k, false, 1:k);
 assert (size(x), [1 k]);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/tiedrank.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/tiedrank.m
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20]);
 assert (r, [1, 2.5, 4, 5, 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10; 20; 30; 40; 20]);
 assert (r, [1; 2.5; 4; 5; 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1);
 assert (r, [1, 2.5, 4, 5, 2.5]);
 assert (tieadj, [1; 0; 18]);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 0, 1);
 assert (r, [1, 2.5, 2, 1, 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1, 1);
 assert (r, [1, 2.5, 2, 1, 2.5]);
 assert (tieadj, [1; 0; 18]);
***** error <tiedrank: X must be a vector.> tiedrank (ones (2))
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], [1, 1])
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], "A")
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], [true, true])
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, [1, 1])
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, "A")
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, [true, true])
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/fitlm.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitlm.m
***** demo
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");
***** demo
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};

 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "CategoricalVars",[1,2],"display","on");
***** test
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 [TAB,STATS] = fitlm (X,y,"continuous",[],"display","off");
 [TAB,STATS] = fitlm (X,y,"CategoricalVars",1,"display","off");
 [TAB,STATS] = fitlm (X,y,"constant","categorical",1,"display","off");
 [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off");
 [TAB,STATS] = fitlm (X,y,[0,0;1,0],"categorical",1,"display","off");
 assert (TAB{2,2}, 10, 1e-04);
 assert (TAB{3,2}, 7.99999999999999, 1e-09);
 assert (TAB{4,2}, 8.99999999999999, 1e-09);
 assert (TAB{5,2}, 11.0001428571429, 1e-09);
 assert (TAB{6,2}, 19.0001111111111, 1e-09);
 assert (TAB{2,3}, 1.01775379540949, 1e-09);
 assert (TAB{3,3}, 1.64107868458008, 1e-09);
 assert (TAB{4,3}, 1.43932122062479, 1e-09);
 assert (TAB{5,3}, 1.48983900477565, 1e-09);
 assert (TAB{6,3}, 1.3987687997822, 1e-09);
 assert (TAB{2,6}, 9.82555903510687, 1e-09);
 assert (TAB{3,6}, 4.87484242844031, 1e-09);
 assert (TAB{4,6}, 6.25294748040552, 1e-09);
 assert (TAB{5,6}, 7.38344399756088, 1e-09);
 assert (TAB{6,6}, 13.5834536158296, 1e-09);
 assert (TAB{3,7}, 2.85812420217862e-05, 1e-12);
 assert (TAB{4,7}, 5.22936741204002e-07, 1e-06);
 assert (TAB{5,7}, 2.12794763209106e-08, 1e-07);
 assert (TAB{6,7}, 7.82091664406755e-15, 1e-08);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = bsxfun (@times, ones(6,1), [1,2,3]);
 popper = bsxfun (@times, [1;1;1;2;2;2], ones(1,3));

 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "categoricalvars",[1,2],"display","off");
 assert (TAB{2,2}, 5.66666666666667, 1e-09);
 assert (TAB{3,2}, -1.33333333333333, 1e-09);
 assert (TAB{4,2}, -2.16666666666667, 1e-09);
 assert (TAB{5,2}, 1.16666666666667, 1e-09);
 assert (TAB{6,2}, -0.333333333333334, 1e-09);
 assert (TAB{7,2}, -0.166666666666667, 1e-09);
 assert (TAB{2,3}, 0.215165741455965, 1e-09);
 assert (TAB{3,3}, 0.304290309725089, 1e-09);
 assert (TAB{4,3}, 0.304290309725089, 1e-09);
 assert (TAB{5,3}, 0.304290309725089, 1e-09);
 assert (TAB{6,3}, 0.43033148291193, 1e-09);
 assert (TAB{7,3}, 0.43033148291193, 1e-09);
 assert (TAB{2,6}, 26.3362867542108, 1e-09);
 assert (TAB{3,6}, -4.38178046004138, 1e-09);
 assert (TAB{4,6}, -7.12039324756724, 1e-09);
 assert (TAB{5,6}, 3.83405790253621, 1e-09);
 assert (TAB{6,6}, -0.774596669241495, 1e-09);
 assert (TAB{7,6}, -0.387298334620748, 1e-09);
 assert (TAB{2,7}, 5.49841502258254e-12, 1e-09);
 assert (TAB{3,7}, 0.000893505495903642, 1e-09);
 assert (TAB{4,7}, 1.21291454302428e-05, 1e-09);
 assert (TAB{5,7}, 0.00237798044119407, 1e-09);
 assert (TAB{6,7}, 0.453570536021938, 1e-09);
 assert (TAB{7,7}, 0.705316781644046, 1e-09);
 ## Test with string ids for categorical variables
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "categoricalvars",[1,2],"display","off");
***** test
 load carsmall
 X = [Weight,Horsepower,Acceleration];
 [TAB, STATS] = fitlm (X, MPG,"constant","display","off");
 [TAB, STATS] = fitlm (X, MPG,"linear","display","off");
 assert (TAB{2,2}, 47.9767628118615, 1e-09);
 assert (TAB{3,2}, -0.00654155878851796, 1e-09);
 assert (TAB{4,2}, -0.0429433065881864, 1e-09);
 assert (TAB{5,2}, -0.0115826516894871, 1e-09);
 assert (TAB{2,3}, 3.87851641748551, 1e-09);
 assert (TAB{3,3}, 0.00112741016370336, 1e-09);
 assert (TAB{4,3}, 0.0243130608813806, 1e-09);
 assert (TAB{5,3}, 0.193325043113178, 1e-09);
 assert (TAB{2,6}, 12.369874881944, 1e-09);
 assert (TAB{3,6}, -5.80228828790225, 1e-09);
 assert (TAB{4,6}, -1.76626492228599, 1e-09);
 assert (TAB{5,6}, -0.0599128364487485, 1e-09);
 assert (TAB{2,7}, 4.89570341688996e-21, 1e-09);
 assert (TAB{3,7}, 9.87424814144e-08, 1e-09);
 assert (TAB{4,7}, 0.0807803098213114, 1e-09);
 assert (TAB{5,7}, 0.952359384151778, 1e-09);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/logit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/logit.m
***** test
 p = [0.01:0.01:0.99];
 assert (logit (p), log (p ./ (1-p)), 25*eps);
***** assert (logit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, +Inf, NaN])
***** error logit ()
***** error logit (1, 2)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/optimalleaforder.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/optimalleaforder.m
***** demo
 randn ("seed", 5)  # for reproducibility
 X = randn (10, 2);
 D = pdist (X);
 tree = linkage(D, 'average');
 optimalleaforder (tree, D, 'Transformation', 'linear')
***** error optimalleaforder ()
***** error optimalleaforder (1)
***** error <tree must be .*> optimalleaforder (ones (2, 2), 1)
***** error <character inputs expected> optimalleaforder ([1 2 3], [1 2; 3 4], "criteria", 5)
***** error <D must be .*> optimalleaforder ([1 2 1], [1 2 3])
***** error <unknown property .*> optimalleaforder ([1 2 1], 1, "xxx", "xxx")
***** error optimalleaforder ([1 2 1], 1, "Transformation", "xxx")
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/ranksum.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ranksum.m
***** test
 mileage = [33.3, 34.5, 37.4; 33.4, 34.8, 36.8; ...
            32.9, 33.8, 37.6; 32.6, 33.4, 36.6; ...
            32.5, 33.7, 37.0; 33.0, 33.9, 36.7];
 [p,h,stats] = ranksum(mileage(:,1),mileage(:,2));
 assert (p, 0.004329004329004329, 1e-14);
 assert (h, true);
 assert (stats.ranksum, 21.5);
***** test
 year1 = [51 52 62 62 52 52 51 53 59 63 59 56 63 74 68 86 82 70 69 75 73 ...
          49 47 50 60 59 60 62 61 71]';
 year2 = [54 53 64 66 57 53 54 54 62 66 59 59 67 76 75 86 82 67 74 80 75 ...
          54 50 53 62 62 62 72 60 67]';
 [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left");
 assert (p, 0.1270832752950605, 1e-14);
 assert (h, false);
 assert (stats.ranksum, 837.5);
 assert (stats.zval, -1.140287483634606, 1e-14);
 [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left", ...
                       "method", "exact");
 assert (p, 0.127343916432862, 1e-14);
 assert (h, false);
 assert (stats.ranksum, 837.5);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/cholcov.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cholcov.m
***** demo
 C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3]
 T = cholcov (C1)
 C2 = T'*T
***** test
 C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3];
 T = cholcov (C1);
 assert (C1, T'*T, 1e-15 * ones (size (C1)));
1 test, 1 passed, 0 known failure, 0 skipped
[inst/levene_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/levene_test.m
***** error<levene_test: invalid number of input arguments.> levene_test ()
***** error<levene_test: invalid number of input arguments.> ...
 levene_test (1, 2, 3, 4, 5);
***** error<levene_test: wrong value for alpha.> levene_test (randn (50, 2), 0);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 2), [1, 2, 3]);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 1), ones (55, 1));
***** error<levene_test: invalid second input argument.> ...
 levene_test (randn (50, 1), ones (50, 2));
***** error<levene_test: wrong value for alpha.> ...
 levene_test (randn (50, 2), [], 1.2);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 2), "some_string");
***** error<levene_test: invalid third input argument.> ...
 levene_test (randn (50, 2), [], "alpha");
***** error<levene_test: wrong value for alpha.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
***** error<levene_test: invalid third input argument.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
***** error<levene_test: invalid option for TESTTYPE as 4th argument.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 0.05, "type");
***** warning<levene_test: GROUP> ...
 levene_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades);
 assert (h, 1);
 assert (pval, 9.523239714592791e-07, 1e-14);
 assert (W, 8.59529, 1e-5);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades, [], "quadratic");
 assert (h, 1);
 assert (pval, 9.523239714592791e-07, 1e-14);
 assert (W, 8.59529, 1e-5);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades, [], "median");
 assert (h, 1);
 assert (pval, 1.312093241723211e-06, 1e-14);
 assert (W, 8.415969, 1e-6);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[1:3]));
 assert (h, 1);
 assert (pval, 0.004349390980463497, 1e-14);
 assert (W, 5.52139, 1e-5);
 assert (df, [2, 357]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[1:3]), "median");
 assert (h, 1);
 assert (pval, 0.004355216763951453, 1e-14);
 assert (W, 5.52001, 1e-5);
 assert (df, [2, 357]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[3,4]), "quadratic");
 assert (h, 0);
 assert (pval, 0.1807494957440653, 2e-14);
 assert (W, 1.80200, 1e-5);
 assert (df, [1, 238]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[3,4]), "median");
 assert (h, 0);
 assert (pval, 0.1978225622063785, 2e-14);
 assert (W, 1.66768, 1e-5);
 assert (df, [1, 238]);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/binotest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/binotest.m
***** demo
 % flip a coin 1000 times, showing 475 heads
 % Hypothesis: coin is fair, i.e. p=1/2
 [h,p_val,ci] = binotest(475,1000,0.5)
 % Result: h = 0 : null hypothesis not rejected, coin could be fair
 %         P value 0.12, i.e. hypothesis not rejected for alpha up to 12%
 %         0.444 <= p <= 0.506 with 95% confidence
***** demo
 % flip a coin 100 times, showing 65 heads
 % Hypothesis: coin shows less than 50% heads, i.e. p<=1/2
 [h,p_val,ci] = binotest(65,100,0.5,'tail','left','alpha',0.01)
 % Result: h = 1 : null hypothesis is rejected, i.e. coin shows more heads than tails
 %         P value 0.0018, i.e. hypothesis not rejected for alpha up to 0.18%
 %         0 <= p <= 0.76 with 99% confidence
***** test #example from https://en.wikipedia.org/wiki/Binomial_test
 [h,p_val,ci] = binotest (51,235,1/6);
 assert (p_val, 0.0437, 0.00005)
 [h,p_val,ci] = binotest (51,235,1/6,'tail','left');
 assert (p_val, 0.027, 0.0005)
1 test, 1 passed, 0 known failure, 0 skipped
[inst/hist3.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hist3.m
***** demo
 X = [
    1    1
    1    1
    1   10
    1   10
    5    5
    5    5
    5    5
    5    5
    5    5
    7    3
    7    3
    7    3
   10   10
   10   10];
 hist3 (X)
***** test
 N_exp = [ 0  0  0  5 20
           0  0 10 15  0
           0 15 10  0  0
          20  5  0  0  0];

 n = 100;
 x = [1:n]';
 y = [n:-1:1]';
 D = [x y];
 N = hist3 (D, [4 5]);
 assert (N, N_exp);
***** test
 N_exp = [0  0  0  0  1
          0  0  0  0  1
          0  0  0  0  1
          1  1  1  1 93];

 n = 100;
 x = [1:n]';
 y = [n:-1:1]';
 D = [x y];
 C{1} = [1 1.7 3 4];
 C{2} = [1:5];
 N = hist3 (D, C);
 assert (N, N_exp);
***** test
 D = [1 1; 3 1; 3 3; 3 1];
 [c, nn] = hist3 (D, {0:4, 0:4});
 exp_c = zeros (5);
 exp_c([7 9 19]) = [1 2 1];
 assert (c, exp_c);
 assert (nn, {0:4, 0:4});
***** test
 for i = 10
   assert (size (hist3 (rand (9, 2), "Edges", {[0:.2:1]; [0:.2:1]})), [6 6])
 endfor
***** test
 edge_1 = linspace (0, 10, 10);
 edge_2 = linspace (0, 50, 10);
 [c, nn] = hist3 ([1:10; 1:5:50]', "Edges", {edge_1, edge_2});
 exp_c = zeros (10, 10);
 exp_c([1 12 13 24 35 46 57 68 79 90]) = 1;
 assert (c, exp_c);

 assert (nn{1}, edge_1 + edge_1(2)/2, eps*10^4)
 assert (nn{2}, edge_2 + edge_2(2)/2, eps*10^4)
***** shared X
 X = [
  5  2
  5  3
  1  4
  5  3
  4  4
  1  2
  2  3
  3  3
  5  4
  5  3];
***** test
 N = zeros (10);
 N([1 10 53 56 60 91 98 100]) = [1 1 1 1 3 1 1 1];
 C = {(1.2:0.4:4.8), (2.1:0.2:3.9)};
 assert (nthargout ([1 2], @hist3, X), {N C}, eps*10^3)
***** test
 N = zeros (5, 7);
 N([1 5 17 18 20 31 34 35]) = [1 1 1 1 3 1 1 1];
 C = {(1.4:0.8:4.6), ((2+(1/7)):(2/7):(4-(1/7)))};
 assert (nthargout ([1 2], @hist3, X, [5 7]), {N C}, eps*10^3)
 assert (nthargout ([1 2], @hist3, X, "Nbins", [5 7]), {N C}, eps*10^3)
***** test
 N = [0 1 0; 0 1 0; 0 0 1; 0 0 0];
 C = {(2:5), (2.5:1:4.5)};
 assert (nthargout ([1 2], @hist3, X, "Edges", {(1.5:4.5), (2:4)}), {N C})
***** test
 N = [0 0 1 0 1 0; 0 0 0 1 0 0; 0 0 1 4 2 0];
 C = {(1.2:3.2), (0:5)};
 assert (nthargout ([1 2], @hist3, X, "Ctrs", C), {N C})
 assert (nthargout ([1 2], @hist3, X, C), {N C})
***** test
 [~, C] = hist3 (rand (10, 2), "Edges", {[0 .05 .15 .35 .55 .95],
                                         [-1 .05 .07 .2 .3 .5 .89 1.2]});
 C_exp = {[ 0.025  0.1   0.25   0.45  0.75  1.15], ...
          [-0.475  0.06  0.135  0.25  0.4   0.695  1.045  1.355]};
 assert (C, C_exp, eps*10^2)
***** test
 Xv = repmat ([1:10]', [1 2]);

 ## Test Centers
 assert (hist3 (Xv, "Ctrs", {1:10, 1:10}), eye (10))

 N_exp = eye (6);
 N_exp([1 end]) = 3;
 assert (hist3 (Xv, "Ctrs", {3:8, 3:8}), N_exp)

 N_exp = zeros (8, 6);
 N_exp([1 2 11 20 29 38 47 48]) = [2 1 1 1 1 1 1 2];
 assert (hist3 (Xv, "Ctrs", {2:9, 3:8}), N_exp)

 ## Test Edges
 assert (hist3 (Xv, "Edges", {1:10, 1:10}), eye (10))
 assert (hist3 (Xv, "Edges", {3:8, 3:8}), eye (6))
 assert (hist3 (Xv, "Edges", {2:9, 3:8}), [zeros(1, 6); eye(6); zeros(1, 6)])

 N_exp = zeros (14);
 N_exp(3:12, 3:12) = eye (10);
 assert (hist3 (Xv, "Edges", {-1:12, -1:12}), N_exp)

 ## Test for Nbins
 assert (hist3 (Xv), eye (10))
 assert (hist3 (Xv, [10 10]), eye (10))
 assert (hist3 (Xv, "nbins", [10 10]), eye (10))
 assert (hist3 (Xv, [5 5]), eye (5) * 2)

 N_exp = zeros (7, 5);
 N_exp([1 9 10 18 26 27 35]) = [2 1 1 2 1 1 2];
 assert (hist3 (Xv, [7 5]), N_exp)
***** test # bug #51059
 D = [1 1; NaN 2; 3 1; 3 3; 1 NaN; 3 1];
 [c, nn] = hist3 (D, {0:4, 0:4});
 exp_c = zeros (5);
 exp_c([7 9 19]) = [1 2 1];
 assert (c, exp_c)
 assert (nn, {0:4, 0:4})
***** test
 [c, nn] = hist3 ([1 8]);
 exp_c = zeros (10, 10);
 exp_c(6, 6) = 1;
 exp_nn = {-4:5, 3:12};
 assert (c, exp_c)
 assert (nn, exp_nn, eps)

 [c, nn] = hist3 ([1 8], [10 11]);
 exp_c = zeros (10, 11);
 exp_c(6, 6) = 1;
 exp_nn = {-4:5, 3:13};
 assert (c, exp_c)
 assert (nn, exp_nn, eps)
***** test
 [c, nn] = hist3 ([1 NaN; 2 3; 6 9; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_c(2, 1) = 1;
 exp_c(8, 10) = 1;
 exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
***** test
 [c, nn] = hist3 ([1 NaN; 2 NaN; 6 NaN; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_nn = {linspace(1.35, 7.65, 10) NaN(1, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
***** test
 [c, nn] = hist3 ([1 NaN; NaN 3; NaN 9; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/bartlett_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/bartlett_test.m
***** error<bartlett_test: invalid number of input arguments.> bartlett_test ()
***** error<bartlett_test: invalid number of input arguments.> ...
 bartlett_test (1, 2, 3, 4);
***** error<bartlett_test: wrong value for alpha.> bartlett_test (randn (50, 2), 0);
***** error<bartlett_test: GROUP and X mismatch.> ...
 bartlett_test (randn (50, 2), [1, 2, 3]);
***** error<bartlett_test: GROUP and X mismatch.> ...
 bartlett_test (randn (50, 1), ones (55, 1));
***** error<bartlett_test: invalid second input argument.> ...
 bartlett_test (randn (50, 1), ones (50, 2));
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 2), [], 1.2);
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 2), [], "alpha");
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
***** warning<bartlett_test: GROUP> ...
 bartlett_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades);
 assert (h, 1);
 assert (pval, 7.908647337018238e-08, 1e-14);
 assert (chisq, 38.73324, 1e-5);
 assert (df, 4);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[2:4]));
 assert (h, 1);
 assert (pval, 0.01172, 1e-5);
 assert (chisq, 8.89274, 1e-5);
 assert (df, 2);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
 assert (h, 0);
 assert (pval, 0.88118, 1e-5);
 assert (chisq, 0.02234, 1e-5);
 assert (df, 1);
***** test
 load examgrades
 grades = [grades; nan(10, 5)];
 [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
 assert (h, 0);
 assert (pval, 0.88118, 1e-5);
 assert (chisq, 0.02234, 1e-5);
 assert (df, 1);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[2,5]), 0.01);
 assert (h, 0);
 assert (pval, 0.01791, 1e-5);
 assert (chisq, 5.60486, 1e-5);
 assert (df, 1);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/crosstab.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/crosstab.m
***** error crosstab ()
***** error crosstab (1)
***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab (ones (2), [1 1])
***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab ([1 1], ones (2))
***** error <x1, x2 .* must be vectors of the same length> crosstab ([1], [1 2])
***** error <x1, x2 .* must be vectors of the same length> crosstab ([1 2], [1])
***** test
 load carbig
 [table, chisq, p, labels] = crosstab (cyl4, when, org);
 assert (table(2,3,1), 38);
 assert (labels{3,3}, "Japan");
***** test
 load carbig
 [table, chisq, p, labels] = crosstab (cyl4, when, org);
 assert (table(2,3,2), 17);
 assert (labels{1,3}, "USA");
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/Clustering/ClusterCriterion.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/ClusterCriterion.m
***** error <ClusterCriterion: 'x' must be a numeric matrix> ...
 ClusterCriterion ("1", "kmeans", [1:6])
***** error <ClusterCriterion: unknown clustering algorithm 'k'> ...
 ClusterCriterion ([1, 2, 1, 3, 2, 4, 3], "k", [1:6])
***** error <ClusterCriterion: invalid matrix of clustering solutions> ...
 ClusterCriterion ([1, 2, 1; 3, 2, 4], 1, [1:6])
***** error <ClusterCriterion: invalid argument> ...
 ClusterCriterion ([1, 2, 1; 3, 2, 4], ones (2, 2, 2), [1:6])
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/Clustering/SilhouetteEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/SilhouetteEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "silhouette", "KList", [1:6]);
 assert (class (eva), "SilhouetteEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/CalinskiHarabaszEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/CalinskiHarabaszEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
 assert (class (eva), "CalinskiHarabaszEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/DaviesBouldinEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/DaviesBouldinEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "DaviesBouldin", "KList", [1:6]);
 assert (class (eva), "DaviesBouldinEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/GapEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/GapEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas([1:50],:), "kmeans", "gap", "KList", [1:3], ...
                     "referencedistribution", "uniform");
 assert (class (eva), "GapEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/tabulate.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/tabulate.m
***** demo
 ## Generate a frequency table for a vector of data in a cell array
 load patients

 ## Display the first seven entries of the Gender variable
 gender = Gender(1:7)

 ## Compute the equency table that shows the number and
 ## percentage of Male and Female patients
 tabulate (Gender)
***** demo
 ## Create a frequency table for a vector of positive integers
 load patients

 ## Display the first seven entries of the Gender variable
 height = Height(1:7)

 ## Create a frequency table that shows, in its second and third columns,
 ## the number and percentage of patients with a particular height.
 table = tabulate (Height);

 ## Display the first and last seven entries of the frequency table
 first = table(1:7,:)

 last = table(end-6:end,:)
***** demo
 ## Create a frequency table from a character array
 load carsmall

 ## Tabulate the data in the Origin variable, which shows the
 ## country of origin of each car in the data set
 tabulate (Origin)
***** demo
 ## Create a frequency table from a numeric vector with NaN values
 load carsmall

 ## The carsmall dataset contains measurements of 100 cars
 total_cars = length (MPG)
 ## For six cars, the MPG value is missing
 missingMPG = length (MPG(isnan (MPG)))

 ## Create a frequency table using MPG
 tabulate (MPG)
 table = tabulate (MPG);

 ## Only 94 cars were used
 valid_cars = sum (table(:,2))
***** test
 load patients
 table = tabulate (Gender);
 assert (table{1,1}, "Male");
 assert (table{2,1}, "Female");
 assert (table{1,2}, 47);
 assert (table{2,2}, 53);
***** test
 load patients
 table = tabulate (Height);
 assert (table(end-4,:), [68, 15, 15]);
 assert (table(end-3,:), [69, 8, 8]);
 assert (table(end-2,:), [70, 11, 11]);
 assert (table(end-1,:), [71, 10, 10]);
 assert (table(end,:), [72, 4, 4]);
***** error<tabulate: X must be either a numeric vector> tabulate (ones (3))
***** error<tabulate: X must be either a numeric vector> tabulate ({1, 2, 3, 4})
***** error<tabulate: X must be either a numeric vector> ...
 tabulate ({"a", "b"; "a", "c"})
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/fitgmdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitgmdist.m
***** demo
 ## Generate a two-cluster problem
 C1 = randn (100, 2) + 2;
 C2 = randn (100, 2) - 2;
 data = [C1; C2];

 ## Perform clustering
 GMModel = fitgmdist (data, 2);

 ## Plot the result
 figure
 [heights, bins] = hist3([C1; C2]);
 [xx, yy] = meshgrid(bins{1}, bins{2});
 bbins = [xx(:), yy(:)];
 contour (reshape (GMModel.pdf (bbins), size (heights)));
***** demo
 Angle_Theta = [ 30 + 10 * randn(1, 10),  60 + 10 * randn(1, 10) ]';
 nbOrientations = 2;
 initial_orientations = [38.0; 18.0];
 initial_weights = ones (1, nbOrientations) / nbOrientations;
 initial_Sigma = 10 * ones (1, 1, nbOrientations);
 start = struct ("mu", initial_orientations, "Sigma", initial_Sigma, ...
                 "ComponentProportion", initial_weights);
 GMModel_Theta = fitgmdist (Angle_Theta, nbOrientations, "Start", start , ...
                            "RegularizationValue", 0.0001)
***** test
 load fisheriris
 classes = unique (species);
 [~, score] = pca (meas, "NumComponents", 2);
 options.MaxIter = 1000;
 options.TolFun = 1e-6;
 options.Display = "off";
 GMModel = fitgmdist (score, 2, "Options", options);
 assert (isa (GMModel, "gmdistribution"), true);
 assert (GMModel.mu, [1.3212, -0.0954; -2.6424, 0.1909], 1e-4);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/anovan.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/anovan.m
***** demo

 # Two-sample unpaired test on independent samples (equivalent to Student's
 # t-test). Note that the absolute value of t-statistic can be obtained by
 # taking the square root of the reported F statistic. In this example,
 # t = sqrt (1.44) = 1.20.

 score = [54 23 45 54 45 43 34 65 77 46 65]';
 gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
           "female" "female" "female"}';

 [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");
***** demo

 # Two-sample paired test on dependent or matched samples equivalent to a
 # paired t-test. As for the first example, the t-statistic can be obtained by
 # taking the square root of the reported F statistic. Note that the interaction
 # between treatment x subject was dropped from the full model by assigning
 # subject as a random factor (').

 score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
 treatment = {"before" "after"; "before" "after"; "before" "after";
              "before" "after"; "before" "after"}';
 subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';

 [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
                            "model", "full", "random", 2, "sstype", 2, ...
                            "varnames", {"treatment", "subject"}, ...
                            "display", "on");
***** demo

 # One-way ANOVA on the data from a study on the strength of structural beams,
 # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan

 strength = [82 86 79 83 84 85 86 87 74 82 ...
            78 75 76 77 79 79 77 78 82 79]';
 alloy = {"st","st","st","st","st","st","st","st", ...
          "al1","al1","al1","al1","al1","al1", ...
          "al2","al2","al2","al2","al2","al2"}';

 [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
                            "varnames", "alloy");
***** demo

 # One-way repeated measures ANOVA on the data from a study on the number of
 # words recalled by 10 subjects for three time condtions, in Loftus & Masson
 # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
 # between seconds x subject was dropped from the full model by assigning
 # subject as a random factor (').

 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
            1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
 subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
             6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];

 [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
                            "model", "full", "random", 2, "sstype", 2, ...
                            "display", "on", "varnames", {"seconds", "subject"});
***** demo

 # Balanced two-way ANOVA with interaction on the data from a study of popcorn
 # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
 # New York: MacMillan

 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {"Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"};
 popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
           "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};

 [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
                            "display", "on", "model", "full", ...
                            "varnames", {"brands", "popper"});
***** demo

 # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
 # gender and having a college degree on salaries of company employees,
 # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15

 salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
           25 29 27 19 18 21 20 21 22 19]';
 gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
           "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
 degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';

 [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
                            "sstype", 3, "display", "on", "varnames", ...
                            {"gender", "degree"});
***** demo

 # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
 # adding sugar and/or milk on the tendency of coffee to make people babble,
 # in from Navarro (2019): 16.10

 sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
          "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
 milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
         "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
 babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
           5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';

 [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
                            "sstype", 3, "display", "on", ...
                            "varnames", {"sugar", "milk"});
***** demo

 # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
 # of three different drugs, biofeedback and diet on patient blood pressure,
 # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
 # * Missing values introduced to make the sample sizes unequal to test the
 #   calculation of different types of sums-of-squares

 drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
         "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
         "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
         "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
         "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
         "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
 feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
 diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
 BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
       173 194 197 190 176 198 164 190 169 164 176 175;
       186 194 201 215 219 209 164 166 159 182 187 174 ...
       189 194 217 206 199 195 171 173 196 199 180 NaN;
       180 187 199 170 204 194 162 184 183 156 180 173 ...
       202 228 190 206 224 204 205 199 170 160 NaN NaN];

 [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
                                    "model", "full", "sstype", 3, ...
                                    "display", "on", ...
                                    "varnames", {"drug", "feedback", "diet"});
***** demo

 # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
 # factor. The data is from a randomized block design study on the effects
 # of antioxidant treatment on glutathione-S-transferase (GST) levels in
 # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
 # Note that all interactions involving block were dropped from the full model
 # by assigning block as a random factor (').

 measurement = [444 614 423 625 408  856 447 719 ...
                764 831 586 782 609 1002 606 766]';
 strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
          "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
 treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
 block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';

 [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
                            "sstype", 2, "model", "full", "random", 3, ...
                            "display", "on", ...
                            "varnames", {"strain", "treatment", "block"});
***** demo

 # One-way ANCOVA on data from a study of the additive effects of species
 # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
 # Text eveR

 pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
          98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
          60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
 temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
         29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
         21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
 species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
            "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
            "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};

 [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
                           "continuous", 2, "sstype", "h", "display", "on", ...
                           "varnames", {"species", "temp"});
***** demo

 # Factorial ANCOVA on data from a study of the effects of treatment and
 # exercise on stress reduction score after adjusting for age. Data from R
 # datarium package).

 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
          97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
          81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
          84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
          100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
          91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
 exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
             "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
        58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
        75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
                            "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
                            "continuous", 3, "sstype", "h", "display", "on", ...
                            "varnames", {"treatment", "exercise", "age"});
***** demo

 # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
 # relating to the contrasts are shown in the table of model parameters, and
 # can be retrieved from the STATS.coeffs output.

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 C = [ 0.4001601  0.3333333  0.5  0.0
       0.4001601  0.3333333 -0.5  0.0
       0.4001601 -0.6666667  0.0  0.0
      -0.6002401  0.0000000  0.0  0.5
      -0.6002401  0.0000000  0.0 -0.5];

 [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
                          "alpha", 0.05, "display", "on");
***** demo

 # One-way ANOVA with the linear model fit by weighted least squares to
 # account for heteroskedasticity. In this example, the variance appears
 # proportional to the outcome, so weights have been estimated by initially
 # fitting the model without weights and regressing the absolute residuals on
 # the fitted values. Although this data could have been analysed by Welch's
 # ANOVA test, the approach here can generalize to ANOVA models with more than
 # one factor.

 g = [1, 1, 1, 1, 1, 1, 1, 1, ...
      2, 2, 2, 2, 2, 2, 2, 2, ...
      3, 3, 3, 3, 3, 3, 3, 3]';
 y = [13, 16, 16,  7, 11,  5,  1,  9, ...
      10, 25, 66, 43, 47, 56,  6, 39, ...
      11, 39, 26, 35, 25, 14, 24, 17]';

 [P,ATAB,STATS] = anovan(y, g, "display", "off");
 fitted = STATS.X * STATS.coeffs(:,1); # fitted values
 b = polyfit (fitted, abs (STATS.resid), 1);
 v = polyval (b, fitted);  # Variance as a function of the fitted values
 figure("Name", "Regression of the absolute residuals on the fitted values");
 plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
 xlabel("Fitted values"); ylabel("Absolute residuals");

 [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);
***** test
 score = [54 23 45 54 45 43 34 65 77 46 65]';
 gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
           'female' 'female' 'female'}';

 [P, T, STATS] = anovan (score,gender,'display','off');
 assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
 assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
 assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
 assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
***** test
 score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
 treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
              'before' 'after'; 'before' 'after'}';
 subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';

 [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
 assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
 assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
 assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
 assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
***** test
 strength = [82 86 79 83 84 85 86 87 74 82 ...
            78 75 76 77 79 79 77 78 82 79]';
 alloy = {'st','st','st','st','st','st','st','st', ...
          'al1','al1','al1','al1','al1','al1', ...
          'al2','al2','al2','al2','al2','al2'}';

 [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
 assert (P(1), 0.000152643638830491, 1e-09);
 assert (ATAB{2,6}, 15.4, 1e-09);
***** test
 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
             6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
 seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
            1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];

 [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
 assert (P(1), 1.51865926758752e-07, 1e-09);
 assert (ATAB{2,2}, 52.2666666666667, 1e-09);
 assert (ATAB{3,2}, 942.533333333333, 1e-09);
 assert (ATAB{4,2}, 11.0666666666667, 1e-09);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};

 [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
 assert (P(1), 7.67895738278171e-07, 1e-09);
 assert (P(2), 0.000100373896304998, 1e-09);
 assert (P(3), 0.746215396636649, 1e-09);
 assert (ATAB{2,6}, 56.7, 1e-09);
 assert (ATAB{3,6}, 32.4, 1e-09);
 assert (ATAB{4,6}, 0.29999999999997, 1e-09);
***** test
 salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
           25 29 27 19 18 21 20 21 22 19]';
 gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
           'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
 degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';

 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
 assert (P(1), 0.747462549227232, 1e-09);
 assert (P(2), 1.03809316857694e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 0.296969696969699, 1e-09);
 assert (ATAB{3,2}, 272.391841491841, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
 assert (P(1), 2.53445097305047e-08, 1e-09);
 assert (P(2), 0.00388133678528749, 1e-09);
 assert (P(3), 0.523689833702671, 1e-09);
 assert (ATAB{2,2}, 242.227272727273, 1e-09);
 assert (ATAB{3,2}, 30.4615384615384, 1e-09);
 assert (ATAB{4,2}, 1.17482517482523, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
 assert (P(1), 0.00388133678528743, 1e-09);
 assert (P(2), 1.03809316857694e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 30.4615384615385, 1e-09);
 assert (ATAB{3,2}, 272.391841491841, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
 assert (P(1), 0.00442898146583742, 1e-09);
 assert (P(2), 1.30634252053587e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 29.3706293706294, 1e-09);
 assert (ATAB{3,2}, 264.335664335664, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
***** test
 sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
          'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
 milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
         'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
 babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
           5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';

 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
 assert (P(1), 0.0108632139833963, 1e-09);
 assert (P(2), 0.0810606976703546, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 3.55752380952381, 1e-09);
 assert (ATAB{3,2}, 0.956108477471702, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
 [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
 assert (P(1), 0.0373333189297505, 1e-09);
 assert (P(2), 0.017075098787169, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 1.444, 1e-09);
 assert (ATAB{3,2}, 3.06963228699552, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
 assert (P(1), 0.017075098787169, 1e-09);
 assert (P(2), 0.0810606976703546, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 3.06963228699552, 1e-09);
 assert (ATAB{3,2}, 0.956108477471702, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625,  1e-09);
 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
 assert (P(1), 0.0454263063473954, 1e-09);
 assert (P(2), 0.0746719907091438, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 2.13184977578476, 1e-09);
 assert (ATAB{3,2}, 1.00413461538462, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
***** test
 drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
         'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
         'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
         'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
         'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
         'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
 feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
 diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
 BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
       173 194 197 190 176 198 164 190 169 164 176 175;
       186 194 201 215 219 209 164 166 159 182 187 174 ...
       189 194 217 206 199 195 171 173 196 199 180 NaN;
       180 187 199 170 204 194 162 184 183 156 180 173 ...
       202 228 190 206 224 204 205 199 170 160 NaN NaN];

 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
 assert (P(1), 7.02561843825325e-05, 1e-09);
 assert (P(2), 0.000425806013389362, 1e-09);
 assert (P(3), 6.16780773446401e-07, 1e-09);
 assert (P(4), 0.261347622678438, 1e-09);
 assert (P(5), 0.0542278432357043, 1e-09);
 assert (P(6), 0.590353225626655, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3614.70355731226, 1e-09);
 assert (ATAB{3,2}, 2227.46639771024, 1e-09);
 assert (ATAB{4,2}, 5008.25614451819, 1e-09);
 assert (ATAB{5,2}, 437.066007908781, 1e-09);
 assert (ATAB{6,2}, 976.180770397332, 1e-09);
 assert (ATAB{7,2}, 46.616653365254, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8,  1e-09);
 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
 assert (P(1), 9.4879638470754e-05, 1e-09);
 assert (P(2), 0.00124177666315809, 1e-09);
 assert (P(3), 6.86162012732911e-07, 1e-09);
 assert (P(4), 0.260856132341256, 1e-09);
 assert (P(5), 0.0523758623892078, 1e-09);
 assert (P(6), 0.590353225626655, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3481.72176560122, 1e-09);
 assert (ATAB{3,2}, 1837.08812970469, 1e-09);
 assert (ATAB{4,2}, 4957.20277938622, 1e-09);
 assert (ATAB{5,2}, 437.693674777847, 1e-09);
 assert (ATAB{6,2}, 988.431929811402, 1e-09);
 assert (ATAB{7,2}, 46.616653365254, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8,  1e-09);
 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
 assert (P(1), 0.000106518678028207, 1e-09);
 assert (P(2), 0.00125371366571508, 1e-09);
 assert (P(3), 5.30813260778464e-07, 1e-09);
 assert (P(4), 0.308353667232981, 1e-09);
 assert (P(5), 0.0562901327343161, 1e-09);
 assert (P(6), 0.599091042141092, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3430.88156424581, 1e-09);
 assert (ATAB{3,2}, 1833.68031496063, 1e-09);
 assert (ATAB{4,2}, 5080.48346456693, 1e-09);
 assert (ATAB{5,2}, 382.07709497207, 1e-09);
 assert (ATAB{6,2}, 963.037988826813, 1e-09);
 assert (ATAB{7,2}, 44.4519685039322, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8, 1e-09);
***** test
 measurement = [444 614 423 625 408  856 447 719 ...
                764 831 586 782 609 1002 606 766]';
 strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
          'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
 treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
 block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';

 [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
 assert (P(1), 0.0914352969909372, 1e-09);
 assert (P(2), 5.04077373924908e-05, 1e-09);
 assert (P(4), 0.0283196918836667, 1e-09);
 assert (ATAB{2,2}, 286.132500000002, 1e-09);
 assert (ATAB{3,2}, 2275.29, 1e-09);
 assert (ATAB{4,2}, 1242.5625, 1e-09);
 assert (ATAB{5,2}, 495.905000000001, 1e-09);
 assert (ATAB{6,2}, 207.007499999999, 1e-09);
***** test
 pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
          98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
          60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
 temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
         29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
         21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
 species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
            'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
            'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};

 [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
 assert (P(1), 6.27153318786007e-14, 1e-09);
 assert (P(2), 2.48773241196644e-25, 1e-09);
 assert (ATAB{2,2}, 598.003953318404, 1e-09);
 assert (ATAB{3,2}, 4376.08256843712, 1e-09);
 assert (ATAB{4,2}, 89.3498685376726, 1e-09);
 assert (ATAB{2,6}, 187.399388123951, 1e-09);
 assert (ATAB{3,6}, 1371.35413763454, 1e-09);
***** test
 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
          97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
          81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
          84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
          100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
          91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
 exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
             'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
             'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
             'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
             'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
             'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
        58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
        75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
 assert (P(5), 0.9245630968248468, 1e-09);
 assert (P(6), 0.791115159521822, 1e-09);
 assert (P(7), 0.9296668751457956, 1e-09);
 [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model',[1 0 0; 0 1 0; 0 0 1; 1 1 0],'continuous',3,'sstype','h','display','off');
 assert (P(1), 0.00158132928938933, 1e-09);
 assert (P(2), 2.12537505039986e-07, 1e-09);
 assert (P(3), 0.00390292555160047, 1e-09);
 assert (P(4), 0.0164086580775543, 1e-09);
 assert (ATAB{2,6}, 11.0956027650549, 1e-09);
 assert (ATAB{3,6}, 20.8195665467178, 1e-09);
 assert (ATAB{4,6}, 9.10966630720186, 1e-09);
 assert (ATAB{5,6}, 4.4457923698584, 1e-09);
***** test
 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 C = [ 0.4001601  0.3333333  0.5  0.0
       0.4001601  0.3333333 -0.5  0.0
       0.4001601 -0.6666667  0.0  0.0
      -0.6002401  0.0000000  0.0  0.5
      -0.6002401  0.0000000  0.0 -0.5];

 [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
 assert (STATS.coeffs(1,1), 19.4001, 1e-04);
 assert (STATS.coeffs(2,1), -9.3297, 1e-04);
 assert (STATS.coeffs(3,1), -5.0000, 1e-04);
 assert (STATS.coeffs(4,1), -8.0000, 1e-04);
 assert (STATS.coeffs(5,1), -8.0000, 1e-04);
 assert (STATS.coeffs(1,2), 0.4831, 1e-04);
 assert (STATS.coeffs(2,2), 0.9694, 1e-04);
 assert (STATS.coeffs(3,2), 1.3073, 1e-04);
 assert (STATS.coeffs(4,2), 1.6411, 1e-04);
 assert (STATS.coeffs(5,2), 1.4507, 1e-04);
 assert (STATS.coeffs(1,5), 40.161, 1e-03);
 assert (STATS.coeffs(2,5), -9.624, 1e-03);
 assert (STATS.coeffs(3,5), -3.825, 1e-03);
 assert (STATS.coeffs(4,5), -4.875, 1e-03);
 assert (STATS.coeffs(5,5), -5.515, 1e-03);
 assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
 assert (STATS.coeffs(3,6), 0.000572, 1e-06);
 assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
 assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/hotelling_t2test2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hotelling_t2test2.m
***** error<Invalid call to hotelling_t2test2.  Correct usage> hotelling_t2test2 ();
***** error<Invalid call to hotelling_t2test2.  Correct usage> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
 hotelling_t2test2 (1, [2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
 hotelling_t2test2 (ones (2,2,2), [2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6], 2);
***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6], ones (2,2,2));
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", 1);
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", -0.2);
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", "a");
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", [0.01, 0.05]);
***** error<hotelling_t2test2: invalid Name argument.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "name", 0.01);
***** error<hotelling_t2test2: if X is a vector, Y must also be a vector.> ...
 hotelling_t2test2 (ones (20,1), ones (20,2));
***** error<hotelling_t2test2: X and Y must have the same number of columns.> ...
 hotelling_t2test2 (ones (20,2), ones (25,3));
***** test
 randn ("seed", 1);
 x1 = randn (60000, 5);
 randn ("seed", 5);
 x2 = randn (30000, 5);
 [h, pval, stats] = hotelling_t2test2 (x1, x2);
 assert (h, 0);
 assert (stats.df1, 5);
 assert (stats.df2, 89994);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/princomp.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/princomp.m
***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
***** test
 x=[7 4 3
    4 1 8
    6 3 5
    8 6 1
    8 5 7
    7 2 9
    5 3 3
    9 5 8
    7 4 5
    8 2 2];
 R = corrcoef (x);
 [V, lambda] = eig (R);
 [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
 S = V(:, i) * diag(sqrt(diag(lambda)(i)));
 ## contribution of first 2 PCs to each original variable
***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
 B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
 F = zscore(x)*B;
 [COEFF,SCORE,latent,tsquare] = princomp(zscore(x, 1));
***** assert(tsquare,sumsq(F, 2),1E4*eps);
***** test
 x=[1,2,3;2,1,3]';
 [COEFF,SCORE,latent,tsquare] = princomp(x);
 m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
***** assert(COEFF,m(1:2,:),10*eps);
***** assert(SCORE,-m,10*eps);
***** assert(latent,[1.5;.5],10*eps);
***** assert(tsquare,[4;4;4]/3,10*eps);
***** test
 x=x';
 [COEFF,SCORE,latent,tsquare] = princomp(x);
 m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
 m(:,3) = m(:,3)*sign(COEFF(3,3));
***** assert(COEFF,m,10*eps);
***** assert(SCORE(:,1),-m(1:2,1),10*eps);
***** assert(SCORE(:,2:3),zeros(2),10*eps);
***** assert(latent,[1;0;0],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
***** test
 [COEFF,SCORE,latent,tsquare] = princomp(x, "econ");
***** assert(COEFF,m(:, 1),10*eps);
***** assert(SCORE,-m(1:2,1),10*eps);
***** assert(latent,[1],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/linkage.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/linkage.m
***** shared x, t
 x = reshape (mod (magic (6),5), [], 3);
 t = 1e-6;
***** assert (cond (linkage (pdist (x))),                   34.119045, t);
***** assert (cond (linkage (pdist (x), "complete")),       21.793345, t);
***** assert (cond (linkage (pdist (x), "average")),        27.045012, t);
***** assert (cond (linkage (pdist (x), "weighted")),       27.412889, t);
 lastwarn(); # Clear last warning before the test
***** warning <cluster distances> linkage (pdist (x), "centroid");
***** test
 warning off Octave:clustering
 assert (cond (linkage (pdist (x), "centroid")),      27.457477, t);
 warning on Octave:clustering
***** warning <cluster distances> linkage (pdist (x), "median");
***** test
 warning off Octave:clustering
 assert (cond (linkage (pdist (x), "median")),        27.683325, t);
 warning on Octave:clustering
***** assert (cond (linkage (pdist (x), "ward")),           17.195198, t);
***** assert (cond (linkage (x, "ward", "euclidean")),      17.195198, t);
***** assert (cond (linkage (x, "ward", {"euclidean"})),    17.195198, t);
***** assert (cond (linkage (x, "ward", {"minkowski", 2})), 17.195198, t);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dcov.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dcov.m
***** demo
 base=@(x) (x- min(x))./(max(x)-min(x));
 N = 5e2;
 x = randn (N,1); x = base (x);
 z = randn (N,1); z = base (z);
 # Linear relations
 cy = [1 0.55 0.3 0 -0.3 -0.55 -1];
 ly = x .* cy;
 ly(:,[1:3 5:end]) = base (ly(:,[1:3 5:end]));
 # Correlated Gaussian
 cz = 1 - abs (cy);
 gy = base ( ly + cz.*z);
 # Shapes
 sx      = repmat (x,1,7);
 sy      = zeros (size (ly));
 v       = 2 * rand (size(x,1),2) - 1;
 sx(:,1) = v(:,1); sy(:,1) = cos(2*pi*sx(:,1)) + 0.5*v(:,2).*exp(-sx(:,1).^2/0.5);
 R       =@(d) [cosd(d) sind(d); -sind(d) cosd(d)];
 tmp     = R(35) * v.';
 sx(:,2) = tmp(1,:); sy(:,2) = tmp(2,:);
 tmp     = R(45) * v.';
 sx(:,3) = tmp(1,:); sy(:,3) = tmp(2,:);
 sx(:,4) = v(:,1); sy(:,4) = sx(:,4).^2 + 0.5*v(:,2);
 sx(:,5) = v(:,1); sy(:,5) = 3*sign(v(:,2)).*(sx(:,5)).^2  + v(:,2);
 sx(:,6) = cos (2*pi*v(:,1)) + 0.5*(x-0.5);
 sy(:,6) = sin (2*pi*v(:,1)) + 0.5*(z-0.5);
 sx(:,7) = x + sign(v(:,1)); sy(:,7) = z + sign(v(:,2));
 sy      = base (sy);
 sx      = base (sx);
 # scaled shape
 sc  = 1/3;
 ssy = (sy-0.5) * sc + 0.5;
 n = size (ly,2);
 ym = 1.2;
 xm = 0.5;
 fmt={'horizontalalignment','center'};
 ff = "% .2f";
 figure (1)
 for i=1:n
   subplot(4,n,i);
   plot (x, gy(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (x,gy(:,i))),fmt{:})

   subplot(4,n,i+n);
   plot (x, ly(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (x,ly(:,i))),fmt{:})

   subplot(4,n,i+2*n);
   plot (sx(:,i), sy(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (sx(:,i),sy(:,i))),fmt{:})
   v = axis ();

   subplot(4,n,i+3*n);
   plot (sx(:,i), ssy(:,i), '.b');
   axis (v)
   axis off
   text (xm,ym,sprintf (ff, dcov (sx(:,i),ssy(:,i))),fmt{:})
 endfor
***** error dcov (randn (30, 5), randn (25,5))
1 test, 1 passed, 0 known failure, 0 skipped
[inst/manova1.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/manova1.m
***** demo
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin)
***** test
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin);
 assert (d, 3);
 assert (p, [0, 3.140583347827075e-07, 0.007510999577743149, ...
             0.1934100745898493]', [1e-12, 1e-12, 1e-12, 1e-12]');
***** test
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight], Origin);
 assert (d, 2);
 assert (p, [0, 0.00516082975137544, 0.1206528056514453]', ...
            [1e-12, 1e-12, 1e-12]');
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/anova2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/anova2.m
***** demo

 # Factorial (Crossed) Two-way ANOVA with Interaction

 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];

 [p, atab, stats] = anova2(popcorn, 3, "on");
***** demo

 # One-way Repeated Measures ANOVA (Rows are a crossed random factor)

 data = [54, 43, 78, 111;
         23, 34, 37, 41;
         45, 65, 99, 78;
         31, 33, 36, 35;
         15, 25, 30, 26];

 [p, atab, stats] = anova2 (data, 1, "on", "linear");
***** demo

 # Balanced Nested One-way ANOVA (Rows are a nested random factor)

 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];

 [p, atab, stats] = anova2 (data, 4, "on", "nested");
***** test
 ## Test for anova2 ("interaction")
 ## comparison with results from Matlab for column effect
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab, stats] = anova2 (popcorn, 3, "off");
 assert (p(1), 7.678957383294716e-07, 1e-14);
 assert (p(2), 0.0001003738963050171, 1e-14);
 assert (p(3), 0.7462153966366274, 1e-14);
 assert (atab{2,5}, 56.700, 1e-14);
 assert (atab{2,3}, 2, 0);
 assert (atab{4,2}, 0.08333333333333348, 1e-14);
 assert (atab{5,4}, 0.1388888888888889, 1e-14);
 assert (atab{5,2}, 1.666666666666667, 1e-14);
 assert (atab{6,2}, 22);
 assert (stats.source, "anova2");
 assert (stats.colmeans, [6.25, 4.75, 4]);
 assert (stats.inter, 1, 0);
 assert (stats.pval, 0.7462153966366274, 1e-14);
 assert (stats.df, 12);
***** test
 ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
 data = [54, 43, 78, 111;
         23, 34, 37, 41;
         45, 65, 99, 78;
         31, 33, 36, 35;
         15, 25, 30, 26];
 [p, atab, stats] = anova2 (data, 1, "off", "linear");
 assert (atab{2,2}, 2174.95, 1e-10);
 assert (atab{3,2}, 8371.7, 1e-10);
 assert (atab{4,2}, 2404.3, 1e-10);
 assert (atab{5,2}, 12950.95, 1e-10);
 assert (atab{2,4}, 724.983333333333, 1e-10);
 assert (atab{3,4}, 2092.925, 1e-10);
 assert (atab{4,4}, 200.358333333333, 1e-10);
 assert (atab{2,5}, 3.61843363972882, 1e-10);
 assert (atab{3,5}, 10.445909412303, 1e-10);
 assert (atab{2,6}, 0.087266112738617, 1e-10);
 assert (atab{3,6}, 0.000698397753556, 1e-10);
***** test
 ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
 [p, atab, stats] = anova2 (data, 4, "off", "nested");
 assert (atab{2,2}, 745.360306290833, 1e-10);
 assert (atab{3,2}, 278.01854140125, 1e-10);
 assert (atab{4,2}, 180.180377467501, 1e-10);
 assert (atab{5,2}, 1203.55922515958, 1e-10);
 assert (atab{2,4}, 372.680153145417, 1e-10);
 assert (atab{3,4}, 92.67284713375, 1e-10);
 assert (atab{4,4}, 10.0100209704167, 1e-10);
 assert (atab{2,5}, 4.02146005730833, 1e-10);
 assert (atab{3,5}, 9.25800729165627, 1e-10);
 assert (atab{2,6}, 0.141597630656771, 1e-10);
 assert (atab{3,6}, 0.000636643812875719, 1e-10);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/pcacov.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pcacov.m
***** demo
 x = [ 7    26     6    60;
       1    29    15    52;
      11    56     8    20;
      11    31     8    47;
       7    52     6    33;
      11    55     9    22;
       3    71    17     6;
       1    31    22    44;
       2    54    18    22;
      21    47     4    26;
       1    40    23    34;
      11    66     9    12;
      10    68     8    12
     ];
 Kxx = cov (x);
 [coeff, latent, explained] = pcacov (Kxx)
***** test
 load hald
 Kxx = cov (ingredients);
 [coeff,latent,explained] = pcacov(Kxx);
 c_out = [-0.0678, -0.6460,  0.5673, 0.5062; ...
          -0.6785, -0.0200, -0.5440, 0.4933; ...
           0.0290,  0.7553,  0.4036, 0.5156; ...
           0.7309, -0.1085, -0.4684, 0.4844];
 l_out = [517.7969; 67.4964; 12.4054; 0.2372];
 e_out = [ 86.5974; 11.2882;  2.0747; 0.0397];
 assert (coeff, c_out, 1e-4);
 assert (latent, l_out, 1e-4);
 assert (explained, e_out, 1e-4);
***** error<pcacov: K must be a square matrix.> pcacov (ones (2,3))
***** error<pcacov: K must be a square matrix.> pcacov (ones (3,3,3))
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/plsregress.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/plsregress.m
***** demo
 ## Perform Partial Least-Squares Regression

 ## Load the spectra data set and use the near infrared (NIR) spectral
 ## intensities (NIR) as the predictor and the corresponding octave
 ## ratings (octave) as the response.
 load spectra

 ## Perform PLS regression with 10 components
 [xload, yload, xscore, yscore, coef, ptcVar] = plsregress (NIR, octane, 10);

 ## Plot the percentage of explained variance in the response variable
 ## (PCTVAR) as a function of the number of components.
 plot (1:10, cumsum (100 * ptcVar(2,:)), "-ro");
 xlim ([1, 10]);
 xlabel ("Number of PLS components");
 ylabel ("Percentage of Explained Variance in octane");
 title ("Explained Variance per PLS components");

 ## Compute the fitted response and display the residuals.
 octane_fitted = [ones(size(NIR,1),1), NIR] * coef;
 residuals = octane - octane_fitted;
 figure
 stem (residuals, "color", "r", "markersize", 4, "markeredgecolor", "r")
 xlabel ("Observations");
 ylabel ("Residuals");
 title ("Residuals in octane's fitted responce");
***** demo
 ## Calculate Variable Importance in Projection (VIP) for PLS Regression

 ## Load the spectra data set and use the near infrared (NIR) spectral
 ## intensities (NIR) as the predictor and the corresponding octave
 ## ratings (octave) as the response.  Variables with a VIP score greater than
 ## 1 are considered important for the projection of the PLS regression model.
 load spectra

 ## Perform PLS regression with 10 components
 [xload, yload, xscore, yscore, coef, pctVar, mse, stats] = ...
                                                 plsregress (NIR, octane, 10);

 ## Calculate the normalized PLS weights
 W0 = stats.W ./ sqrt(sum(stats.W.^2,1));

 ## Calculate the VIP scores for 10 components
 nobs = size (xload, 1);
 SS = sum (xscore .^ 2, 1) .* sum (yload .^ 2, 1);
 VIPscore = sqrt (nobs * sum (SS .* (W0 .^ 2), 2) ./ sum (SS, 2));

 ## Find variables with a VIP score greater than or equal to 1
 VIPidx = find (VIPscore >= 1);

 ## Plot the VIP scores
 scatter (1:length (VIPscore), VIPscore, "xb");
 hold on
 scatter (VIPidx, VIPscore (VIPidx), "xr");
 plot ([1, length(VIPscore)], [1, 1], "--k");
 hold off
 axis ("tight");
 xlabel ("Predictor Variables");
 ylabel ("VIP scores");
 title ("VIP scores for each predictror variable with 10 components");
***** test
 load spectra
 [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 10);
 xload1_out = [-0.0170, 0.0039, 0.0095,  0.0258, 0.0025, ...
               -0.0075, 0.0000, 0.0018, -0.0027, 0.0020];
 yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625, ...
              0.5905, 0.4244, 0.2437, 0.3516, 0.2548];
 xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669,  0.1041, ...
                -0.2067,  0.0457,  0.1565, 0.0706, -0.1471];
 yscore1_out = [-12.4635, -15.0003,  0.0638,  0.0652, -0.0070, ...
                 -0.0634,   0.0062, -0.0012, -0.0151, -0.0173];
 assert (xload(1,:), xload1_out, 1e-4);
 assert (yload, yload_out, 1e-4);
 assert (xscore(1,:), xscore1_out, 1e-4);
 assert (yscore(1,:), yscore1_out, 1e-4);
***** test
 load spectra
 [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 5);
 xload1_out = [-0.0170, 0.0039, 0.0095, 0.0258, 0.0025];
 yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625];
 xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669, 0.1041];
 yscore1_out = [-12.4635, -15.0003, 0.0638, 0.0652, -0.0070];
 assert (xload(1,:), xload1_out, 1e-4);
 assert (yload, yload_out, 1e-4);
 assert (xscore(1,:), xscore1_out, 1e-4);
 assert (yscore(1,:), yscore1_out, 1e-4);
***** error<plsregress: function called with too few input arguments.>
 plsregress (1)
***** error<plsregress: X and Y must be real matrices.> plsregress (1, "asd")
***** error<plsregress: X and Y must be real matrices.> plsregress (1, {1,2,3})
***** error<plsregress: X and Y must be real matrices.> plsregress ("asd", 1)
***** error<plsregress: X and Y must be real matrices.> plsregress ({1,2,3}, 1)
***** error<plsregress: X and Y observations mismatch.> ...
 plsregress (ones (20,3), ones (15,1))
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), 0)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), -5)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), 3.2)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), [2, 3])
***** error<plsregress: NCOMP exceeds maximum components for X.> ...
 plsregress (ones (20,3), ones (20,1), 4)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 4.5)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", -1)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", "somestring")
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", 2.2)
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", -2)
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", [1, 2])
***** error<plsregress: invalid NAME argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "Name", 3, "mcreps", 1)
***** error<plsregress: invalid NAME argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "Name", 1)
***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
 plsregress (ones (20,3), ones (20,1), 3, "mcreps", 2)
***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", "resubstitution", "mcreps", 2)
***** error<Invalid call to plsregress.  Correct usage is:> plsregress (1, 2)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/pca.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pca.m
***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
***** test
 x=[7 4 3
    4 1 8
    6 3 5
    8 6 1
    8 5 7
    7 2 9
    5 3 3
    9 5 8
    7 4 5
    8 2 2];
 R = corrcoef (x);
 [V, lambda] = eig (R);
 [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
 S = V(:, i) * diag(sqrt(diag(lambda)(i)));
***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4); #contribution of first 2 PCs to each original variable
 B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
 F = zscore(x)*B;
 [COEFF,SCORE,latent,tsquare] = pca(zscore(x, 1));
***** assert(tsquare,sumsq(F, 2),1E4*eps);
***** test
 x=[1,2,3;2,1,3]';
 [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
 m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
***** assert(COEFF,m(1:2,:),10*eps);
***** assert(SCORE,-m,10*eps);
***** assert(latent,[1.5;.5],10*eps);
***** assert(tsquare,[4;4;4]/3,10*eps);
 [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false, "weights", [1 2 1], "variableweights", "variance");
***** assert(COEFF, [0.632455532033676 -0.632455532033676; 0.741619848709566 0.741619848709566], 10*eps);
***** assert(SCORE, [-0.622019449426284 0.959119380657905; -0.505649896847432 -0.505649896847431; 1.633319243121148 0.052180413036957], 10*eps);
***** assert(latent, [1.783001790889027; 0.716998209110974], 10*eps);
***** xtest assert(tsquare, [1.5; 0.5; 1.5], 10*eps);  #currently, [4; 2; 4]/3 is actually returned; see comments above
!!!!! known failure
ASSERT errors for:  assert (tsquare,[1.5; 0.5; 1.5],10 * eps)

  Location  |  Observed  |  Expected  |  Reason
    (1)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
    (2)        0.66667        0.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
    (3)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
***** test
 x=x';
 [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
 m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
 m(:,3) = m(:,3)*sign(COEFF(3,3));
***** assert(COEFF,m,10*eps);
***** assert(SCORE(:,1),-m(1:2,1),10*eps);
***** assert(SCORE(:,2:3),zeros(2),10*eps);
***** assert(latent,[1;0;0],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
***** test
 [COEFF,SCORE,latent,tsquare] = pca(x);
***** assert(COEFF,m(:, 1),10*eps);
***** assert(SCORE,-m(1:2,1),10*eps);
***** assert(latent,[1],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
***** error <invalid algorithm> pca([1 2; 3 4], "Algorithm", "xxx")
***** error <'centered' requires a boolean value> pca([1 2; 3 4], "Centered", "xxx")
***** error <must be a positive integer> pca([1 2; 3 4], "NumComponents", -4)
***** error <invalid value for rows> pca([1 2; 3 4], "Rows", 1)
***** error <weights must be> pca([1 2; 3 4], "Weights", [1 2 3])
***** error <weights must be> pca([1 2; 3 4], "Weights", [-1 2])
***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", [-1 2])
***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", "xxx")
***** error <unknown property> pca([1 2; 3 4], "XXX", 1)
32 tests, 31 passed, 1 known failure, 0 skipped
[inst/anova1.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/anova1.m
***** demo
 x = meshgrid (1:6);
 randn ("seed", 15);    # for reproducibility
 x = x + normrnd (0, 1, 6, 6);
 anova1 (x, [], 'off');
***** demo
 x = meshgrid (1:6);
 randn ("seed", 15);    # for reproducibility
 x = x + normrnd (0, 1, 6, 6);
 [p, atab] = anova1(x);
***** demo
 x = ones (50, 4) .* [-2, 0, 1, 5];
 randn ("seed", 13);    # for reproducibility
 x = x + normrnd (0, 2, 50, 4);
 groups = {"A", "B", "C", "D"};
 anova1 (x, groups);
***** demo
 y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
 g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
 anova1 (y(:), g(:), "on", "unequal");
***** test
 data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
         0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
         0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
         1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
         0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
         1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
         0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
         0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
         1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
         0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
 group = [1:10] .* ones (10,10);
 group = group(:);
 [p, tbl] = anova1 (data, group, "off");
 assert (p, 0.022661, 1e-6);
 assert (tbl{2,5}, 2.2969, 1e-4);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 0.003903, 1e-6);
 data = reshape (data, 10, 10);
 [p, tbl, stats] = anova1 (data, [], "off");
 assert (p, 0.022661, 1e-6);
 assert (tbl{2,5}, 2.2969, 1e-4);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 0.003903, 1e-6);
 means = [0.998, 0.9991, 0.9954, 0.9982, 0.9919, 0.9988, 1.0015, 1.0004, 0.9983, 0.9948];
 N = 10 * ones (1, 10);
 assert (stats.means, means, 1e-6);
 assert (length (stats.gnames), 10, 0);
 assert (stats.n, N, 0);
***** test
 y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
 g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
 [p, tbl] = anova1 (y(:), g(:), "off", "equal");
 assert (p, 0.00004163, 1e-6);
 assert (tbl{2,5}, 22.573418, 1e-6);
 assert (tbl{2,3}, 2, 0);
 assert (tbl{3,3}, 14, 0);
 [p, tbl] = anova1 (y(:), g(:), "off", "unequal");
 assert (p, 0.00208877, 1e-8);
 assert (tbl{2,5}, 15.523192, 1e-6);
 assert (tbl{2,3}, 2, 0);
 assert (tbl{2,4}, 7.5786897, 1e-6);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dendrogram.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dendrogram.m
***** demo
 ## simple dendrogram
 y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
 y(:,3) = 1:5;
 dendrogram (y);
 title ("simple dendrogram");
***** demo
 ## another simple dendrogram
 v = 2 * rand (30, 1) - 1;
 d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
 y = linkage (squareform (d, "tovector"));
 dendrogram (y);
 title ("another simple dendrogram");
***** demo
 ## collapsed tree, find all the leaves of node 5
 X = randn (60, 2);
 D = pdist (X);
 y = linkage (D, "average");
 subplot (2, 1, 1);
 title ("original tree");
 dendrogram (y, 0);
 subplot (2, 1, 2);
 title ("collapsed tree");
 [~, t] = dendrogram (y, 20);
 find(t == 5)
***** demo
 ## optimal leaf order
 X = randn (30, 2);
 D = pdist (X);
 y = linkage (D, "average");
 order = optimalleaforder (y, D);
 subplot (2, 1, 1);
 title ("original leaf order");
 dendrogram (y);
 subplot (2, 1, 2);
 title ("optimal leaf order");
 dendrogram (y, "Reorder", order);
***** demo
 ## horizontal orientation and labels
 X = randn (8, 2);
 D = pdist (X);
 L = ["Snow White"; "Doc"; "Grumpy"; "Happy"; "Sleepy"; "Bashful"; ...
      "Sneezy"; "Dopey"];
 y = linkage (D, "average");
 dendrogram (y, "Orientation", "left", "Labels", L);
 title ("horizontal orientation and labels");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 hf = figure ("visible", "off");
 unwind_protect
   y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
   y(:,3) = 1:5;
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
   y(:,3) = 1:5;
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   v = 2 * rand (30, 1) - 1;
   d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
   y = linkage (squareform (d, "tovector"));
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   X = randn (30, 2);
   D = pdist (X);
   y = linkage (D, "average");
   order = optimalleaforder (y, D);
   subplot (2, 1, 1);
   title ("original leaf order");
   dendrogram (y);
   subplot (2, 1, 2);
   title ("optimal leaf order");
   dendrogram (y, "Reorder", order);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error dendrogram ();
***** error <tree must be .*> dendrogram (ones (2, 2), 1);
***** error <unknown property .*> dendrogram ([1 2 1], 1, "xxx", "xxx");
***** error <reorder.*> dendrogram ([1 2 1], "Reorder", "xxx");
***** error <reorder.*> dendrogram ([1 2 1], "Reorder", [1 2 3 4]);
 fail ('dendrogram ([1 2 1], "Orientation", "north")', "invalid orientation .*")
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/hmmgenerate.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hmmgenerate.m
***** test
 len = 25;
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 [sequence, states] = hmmgenerate (len, transprob, outprob);
 assert (length (sequence), len);
 assert (length (states), len);
 assert (min (sequence) >= 1);
 assert (max (sequence) <= columns (outprob));
 assert (min (states) >= 1);
 assert (max (states) <= rows (transprob));
***** test
 len = 25;
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 [sequence, states] = hmmgenerate (len, transprob, outprob, ...
                      "symbols", symbols, "statenames", statenames);
 assert (length (sequence), len);
 assert (length (states), len);
 assert (strcmp (sequence, "A") + strcmp (sequence, "B") + ...
                                  strcmp (sequence, "C") == ones (1, len));
 assert (strcmp (states, "One") + strcmp (states, "Two") == ones (1, len));
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fit/explike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/explike.m
***** test
 x = 12;
 beta = 5;
 [L, V] = explike (beta, x);
 expected_L = 4.0094;
 expected_V = 6.5789;
 assert (L, expected_L, 0.001);
 assert (V, expected_V, 0.001);
***** test
 x = 1:5;
 beta = 2;
 [L, V] = explike (beta, x);
 expected_L = 10.9657;
 expected_V = 0.4;
 assert (L, expected_L, 0.001);
 assert (V, expected_V, 0.001);
***** error<explike: function called with too few input arguments.> explike ()
***** error<explike: function called with too few input arguments.> explike (2)
***** error<explike: MU must be a scalar.> explike ([12, 3], [1:50])
***** error<explike: X must be a vector.> explike (3, ones (10, 2))
***** error<explike: X and CENSOR vectors mismatch.> ...
 explike (3, [1:50], [1, 2, 3])
***** error<explike: X and FREQ vectors mismatch.> ...
 explike (3, [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/bisalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/bisalike.m
***** test
 nlogL = bisalike ([16.2649, 1.0156], [1:50]);
 assert (nlogL, 215.5905, 1e-4);
***** test
 nlogL = bisalike ([2.5585, 0.5839], [1:5]);
 assert (nlogL, 8.9950, 1e-4);
***** error<bisalike: function called with too few input arguments.> bisalike (3.25)
***** error<bisalike: X must be a vector.> bisalike ([5, 0.2], ones (2))
***** error<bisalike: X cannot have negative values.> bisalike ([5, 0.2], [-1, 3])
***** error<bisalike: PARAMS must be a two-element vector.> ...
 bisalike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<bisalike: X and CENSOR vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<bisalike: X and FREQ vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<bisalike: X and FREQ vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/unidfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/unidfit.m
***** demo
 ## Sample 2 populations from different discrete uniform distibutions
 rand ("seed", 1);    # for reproducibility
 r1 = unidrnd (5, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = unidrnd (9, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:20.5, 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 NhatA = unidfit (r(:,1));
 NhatB = unidfit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:10];
 y = unidpdf (x, NhatA);
 plot (x, y, "-pg");
 y = unidpdf (x, NhatB);
 plot (x, y, "-sc");
 xlim ([0, 10])
 ylim ([0, 0.4])
 legend ({"Normalized HIST of sample 1 with N=5", ...
          "Normalized HIST of sample 2 with N=9", ...
          sprintf("PDF for sample 1 with estimated N=%0.2f", NhatA), ...
          sprintf("PDF for sample 2 with estimated N=%0.2f", NhatB)})
 title ("Two population samples from different discrete uniform distibutions")
 hold off
***** test
 x = 0:5;
 [Nhat, Nci] = unidfit (x);
 assert (Nhat, 5);
 assert (Nci, [5; 9]);
***** test
 x = 0:5;
 [Nhat, Nci] = unidfit (x, [], [1 1 1 1 1 1]);
 assert (Nhat, 5);
 assert (Nci, [5; 9]);
***** assert (unidfit ([1 1 2 3]), unidfit ([1 2 3], [] ,[2 1 1]))
***** error<unidfit: function called with too few input arguments.> unidfit ()
***** error<unidfit: X cannot have negative values.> unidfit (-1, [1 2 3 3])
***** error<unidfit: wrong value for ALPHA.> unidfit (1, 0)
***** error<unidfit: wrong value for ALPHA.> unidfit (1, 1.2)
***** error<unidfit: wrong value for ALPHA.> unidfit (1, [0.02 0.05])
***** error<unidfit: X and FREQ vector mismatch.> ...
 unidfit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<unidfit: X and FREQ vector mismatch.> ...
 unidfit ([1.5, 0.2], [], [1, 1, 1])
***** error<unidfit: FREQ cannot have negative values.> ...
 unidfit ([1.5, 0.2], [], [1, -1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/raylfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/raylfit.m
***** demo
 ## Sample 3 populations from 3 different Rayleigh distibutions
 rand ("seed", 2);    # for reproducibility
 r1 = raylrnd (1, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = raylrnd (2, 1000, 1);
 rand ("seed", 3);    # for reproducibility
 r3 = raylrnd (4, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.5:0.5:10.5], 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their lambda parameter
 sigmaA = raylfit (r(:,1));
 sigmaB = raylfit (r(:,2));
 sigmaC = raylfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:10];
 y = raylpdf (x, sigmaA);
 plot (x, y, "-pr");
 y = raylpdf (x, sigmaB);
 plot (x, y, "-sg");
 y = raylpdf (x, sigmaC);
 plot (x, y, "-^c");
 xlim ([0, 10])
 ylim ([0, 0.7])
 legend ({"Normalized HIST of sample 1 with σ=1", ...
          "Normalized HIST of sample 2 with σ=2", ...
          "Normalized HIST of sample 3 with σ=4", ...
          sprintf("PDF for sample 1 with estimated σ=%0.2f", ...
                  sigmaA), ...
          sprintf("PDF for sample 2 with estimated σ=%0.2f", ...
                  sigmaB), ...
          sprintf("PDF for sample 3 with estimated σ=%0.2f", ...
                  sigmaC)})
 title ("Three population samples from different Rayleigh distibutions")
 hold off
***** test
 x = [1 3 2 4 5 4 3 4];
 [shat, sci] = raylfit (x);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.8243; 3.7279], 1e-4)
***** test
 x = [1 3 2 4 5 4 3 4];
 [shat, sci] = raylfit (x, 0.01);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [shat, sci] = raylfit (x, [], [], f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.8243; 3.7279], 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [shat, sci] = raylfit (x, 0.01, [], f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** test
 x = [1 2 3 4 5 6];
 c = [0 0 0 0 0 1];
 f = [1 1 2 3 1 1];
 [shat, sci] = raylfit (x, 0.01, c, f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** error<raylfit: X must be a vector.> raylfit (ones (2,5));
***** error<raylfit: X cannot have negative values.> raylfit ([1 2 -1 3])
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 0)
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 1.2)
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], [0.02 0.05])
***** error<raylfit: X and CENSOR vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<raylfit: X and CENSOR vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<raylfit: X and FREQ vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<raylfit: X and FREQ vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<raylfit: X and FREQ vectors mismatch.>
 raylfit ([1 2 3], [], [], [1 5])
***** error<raylfit: FREQ must not contain negative values.>
 raylfit ([1 2 3], [], [], [1 5 -1])
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/gplike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gplike.m
***** test
 k = 0.8937; sigma = 1.3230; theta = 1;
 x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
      1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
 [nlogL, acov] = gplike ([k, sigma, theta], x);
 assert (nlogL, 21.736, 1e-3);
 assert (acov, [0.7249, -0.7351, 0; -0.7351, 1.3040, 0; 0, 0, 0], 1e-4);
***** assert (gplike ([2, 3, 0], 4), 3.047536764863501, 1e-14)
***** assert (gplike ([2, 3, 4], 8), 3.047536764863501, 1e-14)
***** assert (gplike ([1, 2, 0], 4), 2.890371757896165, 1e-14)
***** assert (gplike ([1, 2, 4], 8), 2.890371757896165, 1e-14)
***** assert (gplike ([2, 3, 0], [1:10]), 32.57864322725392, 1e-14)
***** assert (gplike ([2, 3, 2], [1:10] + 2), 32.57864322725392, 1e-14)
***** assert (gplike ([2, 3, 0], [1:10], ones (1,10)), 32.57864322725392, 1e-14)
***** assert (gplike ([1, 2, 0], [1:10]), 31.65666282460443, 1e-14)
***** assert (gplike ([1, 2, 3], [1:10] + 3), 31.65666282460443, 1e-14)
***** assert (gplike ([1, 2, 0], [1:10], ones (1,10)), 31.65666282460443, 1e-14)
***** assert (gplike ([1, NaN, 0], [1:10]), NaN)
***** error<gplike: function called with too few input arguments.> gplike ()
***** error<gplike: function called with too few input arguments.> gplike (1)
***** error<gplike: X must be a vector.> gplike ([1, 2, 0], [])
***** error<gplike: X must be a vector.> gplike ([1, 2, 0], ones (2))
***** error<gplike: PARAMS must be a three-element vector.> gplike (2, [1:10])
***** error<gplike: PARAMS must be a three-element vector.> gplike ([2, 3], [1:10])
***** error<gplike: X and FREQ vectors mismatch.> ...
 gplike ([1, 2, 0], ones (10, 1), ones (8,1))
***** error<gplike: FREQ must not contain negative values.> ...
 gplike ([1, 2, 0], ones (1, 8), [1 1 1 1 1 1 1 -1])
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fit/ricefit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/ricefit.m
***** demo
 ## Sample 3 populations from different Gamma distibutions
 randg ("seed", 5);    # for reproducibility
 randp ("seed", 6);
 r1 = ricernd (1, 2, 3000, 1);
 randg ("seed", 2);    # for reproducibility
 randp ("seed", 8);
 r2 = ricernd (2, 4, 3000, 1);
 randg ("seed", 7);    # for reproducibility
 randp ("seed", 9);
 r3 = ricernd (7.5, 1, 3000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 75, 4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.7]);
 xlim ([0, 12]);
 hold on

 ## Estimate their α and β parameters
 s_sigmaA = ricefit (r(:,1));
 s_sigmaB = ricefit (r(:,2));
 s_sigmaC = ricefit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01,0.1:0.2:18];
 y = ricepdf (x, s_sigmaA(1), s_sigmaA(2));
 plot (x, y, "-pr");
 y = ricepdf (x, s_sigmaB(1), s_sigmaB(2));
 plot (x, y, "-sg");
 y = ricepdf (x, s_sigmaC(1), s_sigmaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with s=1 and σ=2", ...
          "Normalized HIST of sample 2 with s=2 and σ=4", ...
          "Normalized HIST of sample 3 with s=7.5 and σ=1", ...
          sprintf("PDF for sample 1 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaA(1), s_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaB(1), s_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaC(1), s_sigmaC(2))})
 title ("Three population samples from different Rician distibutions")
 hold off
***** test
 [paramhat, paramci] = ricefit ([1:50]);
 assert (paramhat, [15.3057, 17.6668], 1e-4);
 assert (paramci, [9.5468, 11.7802; 24.5383, 26.4952], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:50], 0.01);
 assert (paramhat, [15.3057, 17.6668], 1e-4);
 assert (paramci, [8.2309, 10.3717; 28.4615, 30.0934], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:5]);
 assert (paramhat, [2.3123, 1.6812], 1e-4);
 assert (paramci, [1.0819, 0.6376; 4.9424, 4.4331], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:5], 0.01);
 assert (paramhat, [2.3123, 1.6812], 1e-4);
 assert (paramci, [0.8521, 0.4702; 6.2747, 6.0120], 1e-4);
***** test
 freq = [1 1 1 1 5];
 [paramhat, paramci] = ricefit ([1:5], [], [], freq);
 assert (paramhat, [3.5181, 1.5565], 1e-4);
 assert (paramci, [2.5893, 0.9049; 4.7801, 2.6772], 1e-4);
***** test
 censor = [1 0 0 0 0];
 [paramhat, paramci] = ricefit ([1:5], [], censor);
 assert (paramhat, [3.2978, 1.1527], 1e-4);
 assert (paramci, [2.3192, 0.5476; 4.6895, 2.4261], 1e-4);
***** assert (class (ricefit (single ([1:50]))), "single")
***** error<ricefit: X must be a vector.> ricefit (ones (2))
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], 1)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], -1)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], {0.05})
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], "k")
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], i)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], [0.01 0.02])
***** error<ricefit: X and CENSOR vectors mismatch.> ricefit ([1:50], [], [1 1])
***** error<ricefit: X and FREQ vectors mismatch.> ricefit ([1:50], [], [], [1 1])
***** error<ricefit: FREQ cannot have negative values.> ...
 ricefit ([1:5], [], [], [1, 1, 2, 1, -1])
***** error<ricefit: X must contain positive values.> ricefit ([1 2 3 -4])
***** error<ricefit: X must contain positive values.> ricefit ([1 2 0], [], [1 0 0])
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fit/gumbellike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gumbellike.m
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([2.3, 1.2], x);
 avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
 assert (nlogL, 3.242264755689906e+17, 1e-14);
 assert (avar, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([2.3, 1.2], x * 0.5);
 avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
 assert (nlogL, 481898704.0472211, 1e-6);
 assert (avar, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([21, 15], x);
 avar_out = [11.73913876598908, -5.9546128523121216; ...
             -5.954612852312121, 3.708060045170236];
 assert (nlogL, 223.7612479380652, 1e-13);
 assert (avar, avar_out, 1e-14);
***** error<gumbellike: too few input arguments.> gumbellike ([12, 15]);
***** error<gumbellike: wrong parameters length.> gumbellike ([12, 15, 3], [1:50]);
***** error<gumbellike: X must be a vector.> gumbellike ([12, 3], ones (10, 2));
***** error<gumbellike: X and CENSOR> gumbellike ([12, 15], [1:50], [1, 2, 3]);
***** error<gumbellike: X and FREQ> gumbellike ([12, 15], [1:50], [], [1, 2, 3]);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/gamlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gamlike.m
***** test
 [nlogL, acov] = gamlike([2, 3], [2, 3, 4, 5, 6, 7, 8, 9]);
 assert (nlogL, 19.4426, 1e-4);
 assert (acov, [2.7819, -5.0073; -5.0073, 9.6882], 1e-4);
***** test
 [nlogL, acov] = gamlike([2, 3], [5:45]);
 assert (nlogL, 305.8070, 1e-4);
 assert (acov, [0.0423, -0.0087; -0.0087, 0.0167], 1e-4);
***** test
 [nlogL, acov] = gamlike([2, 13], [5:45]);
 assert (nlogL, 163.2261, 1e-4);
 assert (acov, [0.2362, -1.6631; -1.6631, 13.9440], 1e-4);
***** error<gamlike: function called with too few input arguments.> ...
 gamlike ([12, 15])
***** error<gamlike: wrong parameters length.> gamlike ([12, 15, 3], [1:50])
***** error<gamlike: X must be a vector.> gamlike ([12, 3], ones (10, 2))
***** error<gamlike: X and CENSOR vectors mismatch.> ...
 gamlike ([12, 15], [1:50], [1, 2, 3])
***** error<gamlike: X and FREQ vectors mismatch.> ...
 gamlike ([12, 15], [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/logifit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/logifit.m
***** demo
 ## Sample 3 populations from different logistic distibutions
 rand ("seed", 5)  # for reproducibility
 r1 = logirnd (2, 1, 2000, 1);
 rand ("seed", 2)   # for reproducibility
 r2 = logirnd (5, 2, 2000, 1);
 rand ("seed", 7)   # for reproducibility
 r3 = logirnd (9, 4, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [-6:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.3]);
 xlim ([-5, 20]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_sA = logifit (r(:,1));
 mu_sB = logifit (r(:,2));
 mu_sC = logifit (r(:,3));

 ## Plot their estimated PDFs
 x = [-5:0.5:20];
 y = logipdf (x, mu_sA(1), mu_sA(2));
 plot (x, y, "-pr");
 y = logipdf (x, mu_sB(1), mu_sB(2));
 plot (x, y, "-sg");
 y = logipdf (x, mu_sC(1), mu_sC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=1 and s=0.5", ...
          "Normalized HIST of sample 2 with μ=2 and s=0.3", ...
          "Normalized HIST of sample 3 with μ=4 and s=0.5", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sA(1), mu_sA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sB(1), mu_sB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sC(1), mu_sC(2))})
 title ("Three population samples from different logistic distibutions")
 hold off
***** test
 paramhat = logifit ([1:50]);
 paramhat_out = [25.5, 8.7724];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:5]);
 paramhat_out = [3, 0.8645];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [3, 0.8645];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = logifit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<logifit: X must be a vector.> logifit (ones (2,5));
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 1.2);
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 0);
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], "alpha");
***** error<logifit: X and CENSOR vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<logifit: X and CENSOR vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<logifit: X and FREQ vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<logifit: X and FREQ vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<logifit: 'options' 5th argument must be a structure> ...
 logifit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/tlslike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/tlslike.m
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [nlogL, acov] = tlslike ([0.035893, 0.862711, 0.649261], x);
 acov_out = [0.2525, 0.0670, 0.0288; ...
             0.0670, 0.5724, 0.1786; ...
             0.0288, 0.1786, 0.1789];
 assert (nlogL, 17.9979636579, 1e-10);
 assert (acov, acov_out, 1e-4);
***** error<tlslike: too few input arguments.> tlslike ([12, 15, 1]);
***** error<tlslike: wrong parameters length.> tlslike ([12, 15], [1:50]);
***** error<tlslike: X must be a vector.> tlslike ([12, 3, 1], ones (10, 2));
***** error<tlslike: X and CENSOR> tlslike ([12, 15, 1], [1:50], [1, 2, 3]);
***** error<tlslike: X and FREQ> tlslike ([12, 15, 1], [1:50], [], [1, 2, 3]);
***** error<tlslike: FREQ cannot> tlslike ([12, 15, 1], [1:3], [], [1, 2, -3]);
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/dist_fit/gpfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gpfit.m
***** demo
 ## Sample 2 populations from different generalized Pareto distibutions
 ## Assume location parameter θ is known
 theta = 0;
 rand ("seed", 5);    # for reproducibility
 r1 = gprnd (1, 2, theta, 20000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = gprnd (3, 1, theta, 20000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.2:100], 5);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "r");
 set (h(2), "facecolor", "c");
 ylim ([0, 1]);
 xlim ([0, 5]);
 hold on

 ## Estimate their α and β parameters
 k_sigmaA = gpfit (r(:,1), theta);
 k_sigmaB = gpfit (r(:,2), theta);

 ## Plot their estimated PDFs
 x = [0.01, 0.1:0.2:18];
 y = gppdf (x, k_sigmaA(1), k_sigmaA(2), theta);
 plot (x, y, "-pc");
 y = gppdf (x, k_sigmaB(1), k_sigmaB(2), theta);
 plot (x, y, "-sr");
 hold off
 legend ({"Normalized HIST of sample 1 with k=1 and σ=2", ...
          "Normalized HIST of sample 2 with k=2 and σ=2", ...
          sprintf("PDF for sample 1 with estimated k=%0.2f and σ=%0.2f", ...
                  k_sigmaA(1), k_sigmaA(2)), ...
          sprintf("PDF for sample 3 with estimated k=%0.2f and σ=%0.2f", ...
                  k_sigmaB(1), k_sigmaB(2))})
 title ("Three population samples from different generalized Pareto distibutions")
 text (2, 0.7, "Known location parameter θ = 0")
 hold off
***** test
 k = 0.8937; sigma = 1.3230; theta = 1;
 x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
      1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
 [hat, ci] = gpfit (x, theta);
 assert (hat, [k, sigma, theta], 1e-4);
 assert (ci, [-0.7750, 0.2437, 1; 2.5624, 7.1820, 1], 1e-4);
***** error<gpfit: function called with too few input arguments.> gpfit ()
***** error<gpfit: function called with too few input arguments.> gpfit (1)
***** error<gpfit: X must be a vector of real values.> gpfit ([0.2, 0.5+i], 0);
***** error<gpfit: X must be a vector of real values.> gpfit (ones (2,2) * 0.5, 0);
***** error<gpfit: THETA must be a real scalar value.> ...
 gpfit ([0.5, 1.2], [0, 1]);
***** error<gpfit: THETA must be a real scalar value.> ...
 gpfit ([0.5, 1.2], 5+i);
***** error<gpfit: X cannot contain values less than THETA.> ...
 gpfit ([1:5], 2);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, 1.2);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, i);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, -1);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
***** error<gpfit: X and FREQ vectors mismatch.>
 gpfit ([1 2 3], 0, [], [1 5])
***** error<gpfit: FREQ must not contain negative values.>
 gpfit ([1 2 3], 0, [], [1 5 -1])
***** error<gpfit: 'options' 5th argument must be a structure> ...
 gpfit ([1:10], 1, 0.05, [], 5)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/gumbelfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gumbelfit.m
***** demo
 ## Sample 3 populations from different Gumbel distibutions
 rand ("seed", 1);    # for reproducibility
 r1 = gumbelrnd (2, 5, 400, 1);
 rand ("seed", 11);    # for reproducibility
 r2 = gumbelrnd (-5, 3, 400, 1);
 rand ("seed", 16);    # for reproducibility
 r3 = gumbelrnd (14, 8, 400, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 25, 0.32);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.28])
 xlim ([-11, 50]);
 hold on

 ## Estimate their MU and BETA parameters
 mu_betaA = gumbelfit (r(:,1));
 mu_betaB = gumbelfit (r(:,2));
 mu_betaC = gumbelfit (r(:,3));

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
 plot (x, y, "-pr");
 y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
 plot (x, y, "-sg");
 y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
          "Normalized HIST of sample 2 with μ=-5 and β=3", ...
          "Normalized HIST of sample 3 with μ=14 and β=8", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaA(1), mu_betaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaB(1), mu_betaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaC(1), mu_betaC(2))})
 title ("Three population samples from different Gumbel distibutions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = gumbelfit (x);
 paramhat_out = [18.3188, 13.0509];
 paramci_out = [14.4882, 10.5294; 22.1495, 16.1763];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = gumbelfit (x, 0.01);
 paramci_out = [13.2845, 9.8426; 23.3532, 17.3051];
 assert (paramci, paramci_out, 1e-4);
***** error<gumbelfit: X must be a double-precision vector.> gumbelfit (ones (2,5));
***** error<gumbelfit: X must be a double-precision vector.> ...
 gumbelfit (single (ones (1,5)));
***** error<gumbelfit: X must NOT contain missing values> ...
 gumbelfit ([1, 2, 3, 4, NaN]);
***** error<gumbelfit: wrong value for ALPHA.> gumbelfit ([1, 2, 3, 4, 5], 1.2);
***** error<gumbelfit: X and CENSOR vectors mismatch.> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<gumbelfit: X and FREQ vectors mismatch.> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [1 1 0]);
***** error<gamfit: FREQ must not contain negative values.>
 gamfit ([1, 2, 3], 0.05, [], [1 5 -1])
***** error<gumbelfit: 'options' 5th argument> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/lognfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/lognfit.m
***** demo
 ## Sample 3 populations from 3 different log-normal distibutions
 randn ("seed", 1);    # for reproducibility
 r1 = lognrnd (0, 0.25, 1000, 1);
 randn ("seed", 2);    # for reproducibility
 r2 = lognrnd (0, 0.5, 1000, 1);
 randn ("seed", 3);    # for reproducibility
 r3 = lognrnd (0, 1, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 30, 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu and sigma parameters
 mu_sigmaA = lognfit (r(:,1));
 mu_sigmaB = lognfit (r(:,2));
 mu_sigmaC = lognfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:6];
 y = lognpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = lognpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 y = lognpdf (x, mu_sigmaC(1), mu_sigmaC(2));
 plot (x, y, "-^c");
 ylim ([0, 2])
 xlim ([0, 6])
 hold off
 legend ({"Normalized HIST of sample 1 with mu=0, σ=0.25", ...
          "Normalized HIST of sample 2 with mu=0, σ=0.5", ...
          "Normalized HIST of sample 3 with mu=0, σ=1", ...
          sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaC(1), mu_sigmaC(2))}, "location", "northeast")
 title ("Three population samples from different log-normal distibutions")
 hold off
***** test
 randn ("seed", 1);
 x = lognrnd (3, 5, [1000, 1]);
 [paramhat, paramci] = lognfit (x, 0.01);
 assert (paramci(1,1) < 3);
 assert (paramci(1,2) > 3);
 assert (paramci(2,1) < 5);
 assert (paramci(2,2) > 5);
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit (ones (20,3))
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit ({1, 2, 3, 4, 5})
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit ([-1, 2, 3, 4, 5])
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), -0.3)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 1.2)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), [0.05,  0.1])
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0.02+i)
***** error<lognfit: X and CENSOR vectors mismatch.> ...
 lognfit (ones (20,1), [], zeros(15,1))
***** error<lognfit: X and FREQ vectors mismatch.> ...
 lognfit (ones (20,1), [], zeros(20,1), ones(25,1))
***** error<lognfit: > lognfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/gamfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gamfit.m
***** demo
 ## Sample 3 populations from different Gamma distibutions
 randg ("seed", 5);    # for reproducibility
 r1 = gamrnd (1, 2, 2000, 1);
 randg ("seed", 2);    # for reproducibility
 r2 = gamrnd (2, 2, 2000, 1);
 randg ("seed", 7);    # for reproducibility
 r3 = gamrnd (7.5, 1, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 75, 4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.62]);
 xlim ([0, 12]);
 hold on

 ## Estimate their α and β parameters
 a_bA = gamfit (r(:,1));
 a_bB = gamfit (r(:,2));
 a_bC = gamfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01,0.1:0.2:18];
 y = gampdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pr");
 y = gampdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sg");
 y = gampdf (x, a_bC(1), a_bC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with α=1 and β=2", ...
          "Normalized HIST of sample 2 with α=2 and β=2", ...
          "Normalized HIST of sample 3 with α=7.5 and β=1", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_bB(1), a_bB(2)), ...
          sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
                  a_bC(1), a_bC(2))})
 title ("Three population samples from different Gamma distibutions")
 hold off
***** shared x
 x = [1.2 1.6 1.7 1.8 1.9 2.0 2.2 2.6 3.0 3.5 4.0 4.8 5.6 6.6 7.6];
***** test
 [paramhat, paramci] = gamfit (x);
 assert (paramhat, [3.4248, 0.9752], 1e-4);
 assert (paramci, [1.7287, 0.4670; 6.7852, 2.0366], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, 0.01);
 assert (paramhat, [3.4248, 0.9752], 1e-4);
 assert (paramci, [1.3945, 0.3705; 8.4113, 2.5668], 1e-4);
***** test
 freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
 [paramhat, paramci] = gamfit (x, [], [], freq);
 assert (paramhat, [3.3025, 1.0615], 1e-4);
 assert (paramci, [1.7710, 0.5415; 6.1584, 2.0806], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, [], [], [1:15]);
 assert (paramhat, [4.4484, 0.9689], 1e-4);
 assert (paramci, [3.4848, 0.7482; 5.6785, 1.2546], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, 0.01, [], [1:15]);
 assert (paramhat, [4.4484, 0.9689], 1e-4);
 assert (paramci, [3.2275, 0.6899; 6.1312, 1.3608], 1e-4);
***** test
 cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
 [paramhat, paramci] = gamfit (x, [], cens, [1:15]);
 assert (paramhat, [4.7537, 0.9308], 1e-4);
 assert (paramci, [3.7123, 0.7162; 6.0872, 1.2097], 1e-4);
***** test
 cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
 freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
 [paramhat, paramci] = gamfit (x, [], cens, freq);
 assert (paramhat, [3.4736, 1.0847], 1e-4);
 assert (paramci, [1.8286, 0.5359; 6.5982, 2.1956], 1e-4);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1]);
 assert (paramhat, [Inf, 0]);
 assert (paramci, [Inf, 0; Inf, 0]);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [1 1 1 1 1 1]);
 assert (paramhat, [NaN, NaN]);
 assert (paramci, [NaN, NaN; NaN, NaN]);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [], [1 1 1 1 1 1]);
 assert (paramhat, [Inf, 0]);
 assert (paramci, [Inf, 0; Inf, 0]);
***** assert (class (gamfit (single (x))), "single")
***** error<gamfit: X must be a vector.> gamfit (ones (2))
***** error<gamfit: wrong value for ALPHA.> gamfit (x, 1)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, -1)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, {0.05})
***** error<gamfit: wrong value for ALPHA.> gamfit (x, "a")
***** error<gamfit: wrong value for ALPHA.> gamfit (x, i)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, [0.01 0.02])
***** error<gamfit: X and FREQ vectors mismatch.>
 gamfit ([1 2 3], 0.05, [], [1 5])
***** error<gamfit: FREQ must not contain negative values.>
 gamfit ([1 2 3], 0.05, [], [1 5 -1])
***** error<gamfit: 'options' 5th argument must be a structure> ...
 gamfit ([1:10], 0.05, [], [], 5)
***** error<gamfit: X cannot contain negative values.> gamfit ([1 2 3 -4])
***** error<gamfit: X must contain positive values when censored.> ...
 gamfit ([1 2 0], [], [1 0 0])
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fit/expfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/expfit.m
***** demo
 ## Sample 3 populations from 3 different exponential distibutions
 rande ("seed", 1);   # for reproducibility
 r1 = exprnd (2, 4000, 1);
 rande ("seed", 2);   # for reproducibility
 r2 = exprnd (5, 4000, 1);
 rande ("seed", 3);   # for reproducibility
 r3 = exprnd (12, 4000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 48, 0.52);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu parameter
 muhat = expfit (r);

 ## Plot their estimated PDFs
 x = [0:max(r(:))];
 y = exppdf (x, muhat(1));
 plot (x, y, "-pr");
 y = exppdf (x, muhat(2));
 plot (x, y, "-sg");
 y = exppdf (x, muhat(3));
 plot (x, y, "-^c");
 ylim ([0, 0.6])
 xlim ([0, 40])
 legend ({"Normalized HIST of sample 1 with μ=2", ...
          "Normalized HIST of sample 2 with μ=5", ...
          "Normalized HIST of sample 3 with μ=12", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f", muhat(1)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f", muhat(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f", muhat(3))})
 title ("Three population samples from different exponential distibutions")
 hold off
***** assert (expfit (1), 1)
***** assert (expfit (1:3), 2)
***** assert (expfit ([1:3]'), 2)
***** assert (expfit (1:3, []), 2)
***** assert (expfit (1:3, [], [], []), 2)
***** assert (expfit (magic (3)), [5 5 5])
***** assert (expfit (cat (3, magic (3), 2*magic (3))), cat (3,[5 5 5], [10 10 10]))
***** assert (expfit (1:3, 0.1, [0 0 0], [1 1 1]), 2)
***** assert (expfit ([1:3]', 0.1, [0 0 0]', [1 1 1]'), 2)
***** assert (expfit (1:3, 0.1, [0 0 0]', [1 1 1]'), 2)
***** assert (expfit (1:3, 0.1, [1 0 0], [1 1 1]), 3)
***** assert (expfit (1:3, 0.1, [0 0 0], [4 1 1]), 1.5)
***** assert (expfit (1:3, 0.1, [1 0 0], [4 1 1]), 4.5)
***** assert (expfit (1:3, 0.1, [1 0 1], [4 1 1]), 9)
***** assert (expfit (1:3, 0.1, [], [-1 1 1]), 4)
***** assert (expfit (1:3, 0.1, [], [0.5 1 1]), 2.2)
***** assert (expfit (1:3, 0.1, [1 1 1]), NaN)
***** assert (expfit (1:3, 0.1, [], [0 0 0]), NaN)
***** assert (expfit (reshape (1:9, [3 3])), [2 5 8])
***** assert (expfit (reshape (1:9, [3 3]), [], eye(3)), [3 7.5 12])
***** assert (expfit (reshape (1:9, [3 3]), [], 2*eye(3)), [3 7.5 12])
***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
 [1.75 4.75 7.75])
***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
 [1.75 4.75 7.75])
***** assert (expfit (reshape (1:9, [3 3]), [], eye(3), [2 2 2; 1 1 1; 1 1 1]), ...
 [3.5 19/3 31/3])
***** assert ([~,muci] = expfit (1:3, 0), [0; Inf])
***** assert ([~,muci] = expfit (1:3, 2), [Inf; 0])
***** assert ([~,muci] = expfit (1:3, 0.1, [1 1 1]), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, 0.1, [], [0 0 0]), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, -1), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, 5), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3), [0.830485728373393; 9.698190330474096], ...
             1000*eps)
***** assert ([~,muci] = expfit (1:3, 0.1), ...
                          [0.953017262058213; 7.337731146400207], 1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4]), ...
             [0.538440777613095, 0.897401296021825, 1.256361814430554; ...
             12.385982973214016, 20.643304955356694, 28.900626937499371], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [1 1 1; 0 0 0]), ...
             100*[0.008132550920455, 0.013554251534091, 0.018975952147727; ...
             1.184936706156216, 1.974894510260360, 2.764852314364504], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [], [3 3 3; 1 1 1]), ...
             [0.570302756652583, 1.026544961974649, 1.482787167296715; ...
             4.587722594914109, 8.257900670845396, 11.928078746776684], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [0 0 0; 1 1 1], [3 3 3; 1 1 1]), ...
             [0.692071440311161, 1.245728592560089, 1.799385744809018; ...
             8.081825275395081, 14.547285495711145, 21.012745716027212], ...
             1000*eps)
***** test
 x = reshape (1:8, [4 2]);
 x(4) = NaN;
 [muhat,muci] = expfit (x);
 assert ({muhat, muci}, {[NaN, 6.5], ...
         [NaN, 2.965574334593430;NaN, 23.856157493553368]}, 1000*eps);
***** test
 x = magic (3);
 censor = [0 1 0; 0 1 0; 0 1 0];
 freq = [1 1 0; 1 1 0; 1 1 0];
 [muhat,muci] = expfit (x, [], censor, freq);
 assert ({muhat, muci}, {[5 NaN NaN], ...
                 [[2.076214320933482; 24.245475826185242],NaN(2)]}, 1000*eps);
***** error expfit ()
***** error expfit (1,2,3,4,5)
***** error [a b censor] = expfit (1)
***** error <ALPHA must be a scalar quantity> expfit (1, [1 2])
***** error <X cannot be negative> expfit ([-1 2 3 4 5])
***** error <CENSOR must be a numeric or logical array> expfit ([1:5], [], "test")
***** error <FREQ must be a numeric or logical array> expfit ([1:5], [], [], "test")
***** error <X and CENSOR vectors mismatch.> expfit ([1:5], [], [0 0 0 0])
***** error <X and FREQ vectors mismatch.> expfit ([1:5], [], [], [1 1 1 1])
47 tests, 47 passed, 0 known failure, 0 skipped
[inst/dist_fit/betafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/betafit.m
***** demo
 ## Sample 2 populations from different Beta distibutions
 randg ("seed", 1);   # for reproducibility
 r1 = betarnd (2, 5, 500, 1);
 randg ("seed", 2);   # for reproducibility
 r2 = betarnd (2, 2, 500, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 12, 15);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their shape parameters
 a_b_A = betafit (r(:,1));
 a_b_B = betafit (r(:,2));

 ## Plot their estimated PDFs
 x = [min(r(:)):0.01:max(r(:))];
 y = betapdf (x, a_b_A(1), a_b_A(2));
 plot (x, y, "-pr");
 y = betapdf (x, a_b_B(1), a_b_B(2));
 plot (x, y, "-sg");
 ylim ([0, 4])
 legend ({"Normalized HIST of sample 1 with α=2 and β=5", ...
          "Normalized HIST of sample 2 with α=2 and β=2", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_b_A(1), a_b_A(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_b_B(1), a_b_B(2))})
 title ("Two population samples from different Beta distibutions")
 hold off
***** test
 x = 0.01:0.02:0.99;
 [paramhat, paramci] = betafit (x);
 paramhat_out = [1.0199, 1.0199];
 paramci_out = [0.6947, 0.6947; 1.4974, 1.4974];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 0.01:0.02:0.99;
 [paramhat, paramci] = betafit (x, 0.01);
 paramci_out = [0.6157, 0.6157; 1.6895, 1.6895];
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 0.00:0.02:1;
 [paramhat, paramci] = betafit (x);
 paramhat_out = [0.0875, 0.1913];
 paramci_out = [0.0822, 0.1490; 0.0931, 0.2455];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** error<betafit: X must be a vector of real values.> betafit ([0.2, 0.5+i]);
***** error<betafit: X must be a vector of real values.> betafit (ones (2,2) * 0.5);
***** error<betafit: X must be in the range> betafit ([0.5, 1.2]);
***** error<betafit: X must contain distinct values.> betafit ([0.1, 0.1]);
***** error<betafit: wrong value for ALPHA.> betafit ([0.01:0.1:0.99], 1.2);
***** error<betafit: X and FREQ vectors mismatch.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2]);
***** error<betafit: FREQ must not contain negative values.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, -1]);
***** error<betafit: FREQ must contain integer values.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, 1.5]);
***** error<betafit: 'options' argument must be a structure> ...
 betafit ([0.01:0.01:0.05], 0.05, struct ("option", 234));
***** error<betafit: 'options' argument must be a structure> ...
 betafit ([0.01:0.01:0.05], 0.05, ones (1,5), struct ("option", 234));
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/wblfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/wblfit.m
***** demo
 ## Sample 3 populations from 3 different Weibull distibutions
 rande ("seed", 1);    # for reproducibility
 r1 = wblrnd(2, 4, 2000, 1);
 rande ("seed", 2);    # for reproducibility
 r2 = wblrnd(5, 2, 2000, 1);
 rande ("seed", 5);    # for reproducibility
 r3 = wblrnd(1, 5, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 30, [2.5 2.1 3.2]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 2]);
 xlim ([0, 10]);
 hold on

 ## Estimate their lambda parameter
 lambda_kA = wblfit (r(:,1));
 lambda_kB = wblfit (r(:,2));
 lambda_kC = wblfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:15];
 y = wblpdf (x, lambda_kA(1), lambda_kA(2));
 plot (x, y, "-pr");
 y = wblpdf (x, lambda_kB(1), lambda_kB(2));
 plot (x, y, "-sg");
 y = wblpdf (x, lambda_kC(1), lambda_kC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with λ=2 and k=4", ...
          "Normalized HIST of sample 2 with λ=5 and k=2", ...
          "Normalized HIST of sample 3 with λ=1 and k=5", ...
          sprintf("PDF for sample 1 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kA(1), lambda_kA(2)), ...
          sprintf("PDF for sample 2 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kB(1), lambda_kB(2)), ...
          sprintf("PDF for sample 3 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kC(1), lambda_kC(2))})
 title ("Three population samples from different Weibull distibutions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = wblfit (x);
 paramhat_out = [28.3636, 1.7130];
 paramci_out = [23.9531, 1.3551; 33.5861, 2.1655];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = wblfit (x, 0.01);
 paramci_out = [22.7143, 1.2589; 35.4179, 2.3310];
 assert (paramci, paramci_out, 1e-4);
***** error<wblfit: X must be a vector.> wblfit (ones (2,5));
***** error<wblfit: X must contain only positive values.> wblfit ([-1 2 3 4]);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 1.2);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 0);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], "alpha");
***** error<wblfit: X and CENSOR vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<wblfit: X and CENSOR vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<wblfit: X and FREQ vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<wblfit: FREQ cannot have negative values.> ...
 wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 -1 1]);
***** error<wblfit: X and FREQ vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<wblfit: 'options' 5th argument must be a structure> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/normlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/normlike.m
***** error<normlike: too few input arguments.> normlike ([12, 15]);
***** error<normlike: X must be a vector.> normlike ([12, 15], ones (2));
***** error<normlike: PARAMS must be a two-element vector.> ...
 normlike ([12, 15, 3], [1:50]);
***** error<normlike: X and CENSOR vectors mismatch.> ...
 normlike ([12, 15], [1:50], [1, 2, 3]);
***** error<normlike: X and FREQ vectors mismatch.> ...
 normlike ([12, 15], [1:50], [], [1, 2, 3]);
***** error<normlike: FREQ must not contain negative values.> ...
 normlike ([12, 15], [1:5], [], [1, 2, 3, 2, -1]);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([2.3, 1.2], x);
 avar_out = [7.5767e-01, -1.8850e-02; -1.8850e-02, 4.8750e-04];
 assert (nlogL, 13014.95883783327, 1e-10);
 assert (avar, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([2.3, 1.2], x * 0.5);
 avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
 assert (nlogL, 2854.802587833265, 1e-10);
 assert (avar, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([21, 15], x);
 avar_out = [5.460474308300396, -1.600790513833993; ...
             -1.600790513833993, 2.667984189723321];
 assert (nlogL, 206.738325604233, 1e-12);
 assert (avar, avar_out, 1e-14);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = normlike ([2.3, 1.2], x, censor);
 avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
 assert (nlogL, Inf);
 assert (avar, [NaN, NaN; NaN, NaN]);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = normlike ([21, 15], x, censor);
 avar_out = [24.4824488866131, -10.6649544179636; ...
             -10.6649544179636, 6.22827849965737];
 assert (nlogL, 86.9254371829733, 1e-12);
 assert (avar, avar_out, 8e-14);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/ricelike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/ricelike.m
***** test
 nlogL = ricelike ([15.3057344, 17.6668458], [1:50]);
 assert (nlogL, 204.5230311010569, 1e-12);
***** test
 nlogL = ricelike ([2.312346885, 1.681228265], [1:5]);
 assert (nlogL, 8.65562164930058, 1e-12);
***** error<ricelike: function called with too few input arguments.> ricelike (3.25)
***** error<ricelike: X must be a vector.> ricelike ([5, 0.2], ones (2))
***** error<ricelike: PARAMS must be a two-element vector.> ...
 ricelike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<ricelike: X and CENSOR vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<ricelike: X and FREQ vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<ricelike: X and FREQ vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1])
***** error<ricelike: FREQ must not contain negative values.> ...
 ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1, 0, -1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/rayllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/rayllike.m
***** test
 x = [1 3 2 4 5 4 3 4];
 [nlogL, acov] = rayllike (3.25, x);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [nlogL, acov] = rayllike (3.25, x, [], f);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5 6];
 f = [1 1 2 3 1 0];
 [nlogL, acov] = rayllike (3.25, x, [], f);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5 6];
 c = [0 0 0 0 0 1];
 f = [1 1 2 3 1 0];
 [nlogL, acov] = rayllike (3.25, x, c, f);
 assert (nlogL, 14.7442, 1e-4)
***** error<rayllike: function called with too few input arguments.> rayllike (1)
***** error<rayllike: SIGMA must be a positive scalar.> rayllike ([1 2 3], [1 2])
***** error<rayllike: X must be a vector of non-negative values.> ...
 rayllike (3.25, ones (10, 2))
***** error<rayllike: X must be a vector of non-negative values.> ...
 rayllike (3.25, [1 2 3 -4 5])
***** error<rayllike: X and CENSOR vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0]);
***** error<rayllike: X and CENSOR vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0 1 1]');
***** error<rayllike: X and FREQ vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], zeros (1,5), [1 1 0]);
***** error<rayllike: X and FREQ vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<rayllike: FREQ must not contain negative values.> ...
 rayllike (3.25, ones (1, 8), [], [1 1 1 1 1 1 1 -1])
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/burrlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/burrlike.m
***** error<burrlike: function called with too few input arguments.> burrlike (3.25)
***** error<burrlike: X must be a vector.> burrlike ([1, 2, 3], ones (2))
***** error<burrlike: X cannot have negative values.> burrlike ([1, 2, 3], [-1, 3])
***** error<burrlike: PARAMS must be a three-element vector.> ...
 burrlike ([1, 2], [1, 3, 5, 7])
***** error<burrlike: PARAMS must be a three-element vector.> ...
 burrlike ([1, 2, 3, 4], [1, 3, 5, 7])
***** error<burrlike: X and CENSOR vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [0, 0, 0])
***** error<burrlike: X and FREQ vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<burrlike: X and FREQ vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/evlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/evlike.m
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([2.3, 1.2], x);
 avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
 assert (nlogL, 3.242264755689906e+17, 1e-14);
 assert (acov, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([2.3, 1.2], x * 0.5);
 avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
 assert (nlogL, 481898704.0472211, 1e-6);
 assert (acov, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([21, 15], x);
 avar_out = [11.73913876598908, -5.9546128523121216; ...
             -5.954612852312121, 3.708060045170236];
 assert (nlogL, 223.7612479380652, 1e-13);
 assert (acov, avar_out, 1e-14);
***** error<evlike: function called with too few input arguments.> evlike ([12, 15])
***** error<evlike: wrong parameters length.> evlike ([12, 15, 3], [1:50])
***** error<evlike: X must be a vector.> evlike ([12, 3], ones (10, 2))
***** error<evlike: X and CENSOR vectors mismatch.> ...
 evlike ([12, 15], [1:50], [1, 2, 3])
***** error<evlike: X and FREQ vectors mismatch.> ...
 evlike ([12, 15], [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gevlike.m
***** test
 x = 1;
 k = 0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 0.75942;
 expected_C = [-0.12547 1.77884 1.06731; 1.77884 16.40761 8.48877; 1.06731 8.48877 0.27979];
 assert (L, expected_L, 0.001);
 assert (C, inv (expected_C), 0.001);
***** test
 x = 1;
 k = 0;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 0.65157;
 expected_C = [0.090036 3.41229 2.047337; 3.412229 24.760027 12.510190; 2.047337 12.510190 2.098618];
 assert (L, expected_L, 0.001);
 assert (C, inv (expected_C), 0.001);
***** test
 x = -5:-1;
 k = -0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 3786.4;
 expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
               4.6110e-06, 7.5693e-06, 1.2034e-05; ...
               8.7297e-05, 1.2034e-05, -0.0019125];
 assert (L, expected_L, -0.001);
 assert (C, expected_C, -0.001);
***** test
 x = -5:0;
 k = -0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x, [1, 1, 1, 1, 1, 0]);
 expected_L = 3786.4;
 expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
               4.6110e-06, 7.5693e-06, 1.2034e-05; ...
               8.7297e-05, 1.2034e-05, -0.0019125];
 assert (L, expected_L, -0.001);
 assert (C, expected_C, -0.001);
***** error<gevlike: function called with too few input arguments.> gevlike (3.25)
***** error<gevlike: X must be a vector.> gevlike ([1, 2, 3], ones (2))
***** error<gevlike: PARAMS must be a three-element vector.> ...
 gevlike ([1, 2], [1, 3, 5, 7])
***** error<gevlike: PARAMS must be a three-element vector.> ...
 gevlike ([1, 2, 3, 4], [1, 3, 5, 7])
***** error<gevlike: X and FREQ vectors mismatch.> ...
 gevlike ([5, 0.2, 1], ones (10, 1), ones (8,1))
***** error<gevlike: FREQ must not contain negative values.> ...
 gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<gevlike: FREQ must contain integer values.> ...
 gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 1.5])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/hnfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/hnfit.m
***** demo
 ## Sample 2 populations from different half-normal distibutions
 rand ("seed", 1);   # for reproducibility
 r1 = hnrnd (0, 5, 5000, 1);
 rand ("seed", 2);   # for reproducibility
 r2 = hnrnd (0, 2, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0.5:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their shape parameters
 mu_sigmaA = hnfit (r(:,1), 0);
 mu_sigmaB = hnfit (r(:,2), 0);

 ## Plot their estimated PDFs
 x = [0:0.2:10];
 y = hnpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = hnpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 xlim ([0, 10])
 ylim ([0, 0.5])
 legend ({"Normalized HIST of sample 1 with μ=0 and σ=5", ...
          "Normalized HIST of sample 2 with μ=0 and σ=2", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2))})
 title ("Two population samples from different half-normal distibutions")
 hold off
***** test
 x = 1:20;
 [paramhat, paramci] = hnfit (x, 0);
 assert (paramhat, [0, 11.9791], 1e-4);
 assert (paramci, [0, 9.1648; 0, 17.2987], 1e-4);
***** test
 x = 1:20;
 [paramhat, paramci] = hnfit (x, 0, 0.01);
 assert (paramci, [0, 8.4709; 0, 19.6487], 1e-4);
***** error<hnfit: function called with too few input arguments.> hnfit ()
***** error<hnfit: function called with too few input arguments.> hnfit (1)
***** error<hnfit: X must be a vector of real values.> hnfit ([0.2, 0.5+i], 0);
***** error<hnfit: X must be a vector of real values.> hnfit (ones (2,2) * 0.5, 0);
***** error<hnfit: MU must be a real scalar value.> ...
 hnfit ([0.5, 1.2], [0, 1]);
***** error<hnfit: MU must be a real scalar value.> ...
 hnfit ([0.5, 1.2], 5+i);
***** error<hnfit: X cannot contain values less than MU.> ...
 hnfit ([1:5], 2);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, 1.2);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, i);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, -1);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
***** error<hnfit: X and FREQ vectors mismatch.>
 hnfit ([1 2 3], 0, [], [1 5])
***** error<hnfit: FREQ must not contain negative values.>
 hnfit ([1 2 3], 0, [], [1 5 -1])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/logllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/logllike.m
***** test
 [nlogL, acov] = logllike ([3.09717, 0.468525], [1:50]);
 assert (nlogL, 211.2965, 1e-4);
 assert (acov, [0.0131, -0.0007; -0.0007, 0.0031], 1e-4);
***** test
 [nlogL, acov] = logllike ([1.01124, 0.336449], [1:5]);
 assert (nlogL, 9.2206, 1e-4);
 assert (acov, [0.0712, -0.0032; -0.0032, 0.0153], 1e-4);
***** error<logllike: function called with too few input arguments.> logllike (3.25)
***** error<logllike: X must be a vector.> logllike ([5, 0.2], ones (2))
***** error<logllike: PARAMS must be a two-element vector.> ...
 logllike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<logllike: X and CENSOR vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<logllike: X and FREQ vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<logllike: X and FREQ vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/betalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/betalike.m
***** test
 x = 0.01:0.02:0.99;
 [nlogL, avar] = betalike ([2.3, 1.2], x);
 avar_out = [0.03691678, 0.02803056; 0.02803056, 0.03965629];
 assert (nlogL, 17.873477715879040, 3e-14);
 assert (avar, avar_out, 1e-7);
***** test
 x = 0.01:0.02:0.99;
 [nlogL, avar] = betalike ([1, 4], x);
 avar_out = [0.02793282, 0.02717274; 0.02717274, 0.03993361];
 assert (nlogL, 79.648061114839550, 1e-13);
 assert (avar, avar_out, 1e-7);
***** test
 x = 0.00:0.02:1;
 [nlogL, avar] = betalike ([1, 4], x);
 avar_out = [0.00000801564765, 0.00000131397245; ...
             0.00000131397245, 0.00070827639442];
 assert (nlogL, 573.2008434477486, 1e-10);
 assert (avar, avar_out, 1e-14);
***** error<betalike: function called with too few input arguments.> ...
 betalike ([12, 15]);
***** error<betalike: wrong parameters length.> betalike ([12, 15, 3], [1:50]);
***** error<betalike: X and FREQ vectors mismatch.> ...
 betalike ([12, 15], ones (10, 1), ones (8,1))
***** error<betalike: FREQ must not contain negative values.> ...
 betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<betalike: FREQ must contain integer values.> ...
 betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 1.5])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/nbinlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/nbinlike.m
***** assert (nbinlike ([2.42086, 0.0867043], [1:50]), 205.5942, 1e-4)
***** assert (nbinlike ([3.58823, 0.254697], [1:20]), 63.6435, 1e-4)
***** assert (nbinlike ([8.80671, 0.615565], [1:10]), 24.7410, 1e-4)
***** assert (nbinlike ([22.1756, 0.831306], [1:8]), 17.9528, 1e-4)
***** assert (nbinlike ([22.1756, 0.831306], [1:9], [ones(1,8), 0]), 17.9528, 1e-4)
***** error<nbinlike: function called with too few input arguments.> nbinlike (3.25)
***** error<nbinlike: X must be a vector.> nbinlike ([5, 0.2], ones (2))
***** error<nbinlike: X cannot have negative values.> nbinlike ([5, 0.2], [-1, 3])
***** error<nbinlike: PARAMS must be a two-element vector.> ...
 nbinlike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<nbinlike: number of successes,> nbinlike ([-5, 0.2], [1:15])
***** error<nbinlike: number of successes,> nbinlike ([0, 0.2], [1:15])
***** error<nbinlike: probability of success,> nbinlike ([5, 1.2], [3, 5])
***** error<nbinlike: probability of success,> nbinlike ([5, -0.2], [3, 5])
***** error<nbinlike: X and FREQ vectors mismatch.> ...
 nbinlike ([5, 0.2], ones (10, 1), ones (8,1))
***** error<nbinlike: FREQ must not contain negative values.> ...
 nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<nbinlike: FREQ must contain integer values.> ...
 nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 1.5])
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/nbinfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/nbinfit.m
***** demo
 ## Sample 2 populations from different negative binomial distibutions
 randp ("seed", 5); randg ("seed", 5);    # for reproducibility
 r1 = nbinrnd (2, 0.15, 5000, 1);
 randp ("seed", 8); randg ("seed", 8);    # for reproducibility
 r2 = nbinrnd (5, 0.2, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0:51], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 r_psA = nbinfit (r(:,1));
 r_psB = nbinfit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:40];
 y = nbinpdf (x, r_psA(1), r_psA(2));
 plot (x, y, "-pg");
 x = [min(r(:,2)):max(r(:,2))];
 y = nbinpdf (x, r_psB(1), r_psB(2));
 plot (x, y, "-sc");
 ylim ([0, 0.1])
 xlim ([0, 50])
 legend ({"Normalized HIST of sample 1 with r=2 and ps=0.15", ...
          "Normalized HIST of sample 2 with r=5 and ps=0.2", ...
          sprintf("PDF for sample 1 with estimated r=%0.2f and ps=%0.2f", ...
                  r_psA(1), r_psA(2)), ...
          sprintf("PDF for sample 2 with estimated r=%0.2f and ps=%0.2f", ...
                  r_psB(1), r_psB(2))})
 title ("Two population samples from negative different binomial distibutions")
 hold off
***** test
 [paramhat, paramci] = nbinfit ([1:50]);
 assert (paramhat, [2.420857, 0.086704], 1e-6);
 assert (paramci(:,1), [1.382702; 3.459012], 1e-6);
 assert (paramci(:,2), [0.049676; 0.123732], 1e-6);
***** test
 [paramhat, paramci] = nbinfit ([1:20]);
 assert (paramhat, [3.588233, 0.254697], 1e-6);
 assert (paramci(:,1), [0.451693; 6.724774], 1e-6);
 assert (paramci(:,2), [0.081143; 0.428251], 1e-6);
***** test
 [paramhat, paramci] = nbinfit ([1:10]);
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** test
 [paramhat, paramci] = nbinfit ([1:10], 0.05, ones (1, 10));
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** test
 [paramhat, paramci] = nbinfit ([1:11], 0.05, [ones(1, 10), 0]);
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** error<nbinfit: X cannot have negative values.> nbinfit ([-1 2 3 3])
***** error<nbinfit: X must be a vector.> nbinfit (ones (2))
***** error<nbinfit: X must be a non-negative integer.> nbinfit ([1 2 1.2 3])
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 0)
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 1.2)
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], [0.02 0.05])
***** error<nbinfit: X and FREQ vectors mismatch.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
***** error<nbinfit: FREQ must not contain negative values.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
***** error<nbinfit: FREQ must contain integer values.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
***** error<nbinfit: 'options' argument must be a structure> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
***** error<nbinfit: 'options' argument must be a structure> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/poissfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/poissfit.m
***** demo
 ## Sample 3 populations from 3 different Poisson distibutions
 randp ("seed", 2);    # for reproducibility
 r1 = poissrnd (1, 1000, 1);
 randp ("seed", 2);    # for reproducibility
 r2 = poissrnd (4, 1000, 1);
 randp ("seed", 3);    # for reproducibility
 r3 = poissrnd (10, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their lambda parameter
 lambdahat = poissfit (r);

 ## Plot their estimated PDFs
 x = [0:20];
 y = poisspdf (x, lambdahat(1));
 plot (x, y, "-pr");
 y = poisspdf (x, lambdahat(2));
 plot (x, y, "-sg");
 y = poisspdf (x, lambdahat(3));
 plot (x, y, "-^c");
 xlim ([0, 20])
 ylim ([0, 0.4])
 legend ({"Normalized HIST of sample 1 with λ=1", ...
          "Normalized HIST of sample 2 with λ=4", ...
          "Normalized HIST of sample 3 with λ=10", ...
          sprintf("PDF for sample 1 with estimated λ=%0.2f", ...
                  lambdahat(1)), ...
          sprintf("PDF for sample 2 with estimated λ=%0.2f", ...
                  lambdahat(2)), ...
          sprintf("PDF for sample 3 with estimated λ=%0.2f", ...
                  lambdahat(3))})
 title ("Three population samples from different Poisson distibutions")
 hold off
***** test
 x = [1 3 2 4 5 4 3 4];
 [lhat, lci] = poissfit (x);
 assert (lhat, 3.25)
 assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
***** test
 x = [1 3 2 4 5 4 3 4];
 [lhat, lci] = poissfit (x, 0.01);
 assert (lhat, 3.25)
 assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [lhat, lci] = poissfit (x, [], f);
 assert (lhat, 3.25)
 assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [lhat, lci] = poissfit (x, 0.01, f);
 assert (lhat, 3.25)
 assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
***** error<poissfit: X cannot have negative values.> poissfit ([1 2 -1 3])
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 0)
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 1.2)
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], [0.02 0.05])
***** error<poissfit: X and FREQ vectors mismatch.>
 poissfit ([1 2 3], [], [1 5])
***** error<poissfit: FREQ must not contain negative values.>
 poissfit ([1 2 3], [], [1 5 -1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/binofit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/binofit.m
***** demo
 ## Sample 2 populations from different binomial distibutions
 rand ("seed", 1);    # for reproducibility
 r1 = binornd (50, 0.15, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = binornd (100, 0.5, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 23, 0.35);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 pshatA = binofit (r(:,1), 50);
 pshatB = binofit (r(:,2), 100);

 ## Plot their estimated PDFs
 x = [min(r(:,1)):max(r(:,1))];
 y = binopdf (x, 50, mean (pshatA));
 plot (x, y, "-pg");
 x = [min(r(:,2)):max(r(:,2))];
 y = binopdf (x, 100, mean (pshatB));
 plot (x, y, "-sc");
 ylim ([0, 0.2])
 legend ({"Normalized HIST of sample 1 with ps=0.15", ...
          "Normalized HIST of sample 2 with ps=0.50", ...
          sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
                  mean (pshatA)), ...
          sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
                  mean (pshatB))})
 title ("Two population samples from different binomial distibutions")
 hold off
***** test
 x = 0:3;
 [pshat, psci] = binofit (x, 3);
 assert (pshat, [0, 0.3333, 0.6667, 1], 1e-4);
 assert (psci(1,:), [0, 0.7076], 1e-4);
 assert (psci(2,:), [0.0084, 0.9057], 1e-4);
 assert (psci(3,:), [0.0943, 0.9916], 1e-4);
 assert (psci(4,:), [0.2924, 1.0000], 1e-4);
***** error<binofit: function called with too few input arguments.> ...
 binofit ([1 2 3 4])
***** error<binofit: X cannot have negative values.> ...
 binofit ([-1, 4, 3, 2], [1, 2, 3, 3])
***** error<binofit: X must be a vector.> binofit (ones(2), [1, 2, 3, 3])
***** error<binofit: N must be a non-negative integer.> ...
 binofit ([1, 4, 3, 2], [1, 2, -1, 3])
***** error<binofit: N must be a scalar or the same size as X.> ...
 binofit ([1, 4, 3, 2], [5, 5, 5])
***** error<binofit: N must be at least as large as X.> ...
 binofit ([1, 4, 3, 2], [5, 3, 5, 5])
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 1.2);
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 0);
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, "alpha");
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/tlsfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/tlsfit.m
***** demo
 ## Sample 3 populations from 3 different location-scale T distibutions
 randn ("seed", 1);    # for reproducibility
 randg ("seed", 2);    # for reproducibility
 r1 = tlsrnd (-4, 3, 1, 2000, 1);
 randn ("seed", 3);    # for reproducibility
 randg ("seed", 4);    # for reproducibility
 r2 = tlsrnd (0, 3, 1, 2000, 1);
 randn ("seed", 5);    # for reproducibility
 randg ("seed", 6);    # for reproducibility
 r3 = tlsrnd (5, 5, 4, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [-21:21], [1, 1, 1]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.25]);
 xlim ([-20, 20]);
 hold on

 ## Estimate their lambda parameter
 mu_sigma_nuA = tlsfit (r(:,1));
 mu_sigma_nuB = tlsfit (r(:,2));
 mu_sigma_nuC = tlsfit (r(:,3));

 ## Plot their estimated PDFs
 x = [-20:0.1:20];
 y = tlspdf (x, mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3));
 plot (x, y, "-pr");
 y = tlspdf (x, mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3));
 plot (x, y, "-sg");
 y = tlspdf (x, mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=0, σ=2 and nu=1", ...
          "Normalized HIST of sample 2 with μ=5, σ=2 and nu=1", ...
          "Normalized HIST of sample 3 with μ=3, σ=4 and nu=3", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3))})
 title ("Three population samples from different location-scale T distibutions")
 hold off
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [paramhat, paramci] = tlsfit (x);
 paramhat_out = [0.035893, 0.862711, 0.649261];
 paramci_out = [-0.949034, 0.154655, 0.181080; 1.02082, 4.812444, 2.327914];
 assert (paramhat, paramhat_out, 1e-6);
 assert (paramci, paramci_out, 1e-5);
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [paramhat, paramci] = tlsfit (x, 0.01);
 paramci_out = [-1.2585, 0.0901, 0.1212; 1.3303, 8.2591, 3.4771];
 assert (paramci, paramci_out, 1e-4);
***** error<tlsfit: X must be a vector.> tlsfit (ones (2,5));
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 1.2);
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 0);
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], "alpha");
***** error<tlsfit: X and CENSOR vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<tlsfit: X and CENSOR vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<tlsfit: X and FREQ vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<tlsfit: X and FREQ vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<tlsfit: FREQ cannot have negative values.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 -1]);
***** error<tlsfit: 'options' 5th argument must be a structure> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/invgfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/invgfit.m
***** demo
 ## Sample 3 populations from different inverse Gaussian distibutions
 rand ("seed", 5); randn ("seed", 5);   # for reproducibility
 r1 = invgrnd (1, 0.2, 2000, 1);
 rand ("seed", 2); randn ("seed", 2);   # for reproducibility
 r2 = invgrnd (1, 3, 2000, 1);
 rand ("seed", 7); randn ("seed", 7);   # for reproducibility
 r3 = invgrnd (3, 1, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.1:3.2], 9);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3]);
 xlim ([0, 3]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_lambdaA = invgfit (r(:,1));
 mu_lambdaB = invgfit (r(:,2));
 mu_lambdaC = invgfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:3];
 y = invgpdf (x, mu_lambdaA(1), mu_lambdaA(2));
 plot (x, y, "-pr");
 y = invgpdf (x, mu_lambdaB(1), mu_lambdaB(2));
 plot (x, y, "-sg");
 y = invgpdf (x, mu_lambdaC(1), mu_lambdaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=1 and λ=0.5", ...
          "Normalized HIST of sample 2 with μ=2 and λ=0.3", ...
          "Normalized HIST of sample 3 with μ=4 and λ=0.5", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaA(1), mu_lambdaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaB(1), mu_lambdaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaC(1), mu_lambdaC(2))})
 title ("Three population samples from different inverse Gaussian distibutions")
 hold off
***** test
 paramhat = invgfit ([1:50]);
 paramhat_out = [25.5, 19.6973];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = invgfit ([1:5]);
 paramhat_out = [3, 8.1081];
 assert (paramhat, paramhat_out, 1e-4);
***** error<invgfit: X must be a vector.> invgfit (ones (2,5));
***** error<invgfit: X must contain only positive values.> invgfit ([-1 2 3 4]);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 1.2);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 0);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], "alpha");
***** error<invgfit: X and CENSOR vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<invgfit: X and CENSOR vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<invgfit: X and FREQ vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<invgfit: X and FREQ vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<invgfit: 'options' 5th argument must be a structure> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/poisslike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/poisslike.m
***** test
 x = [1 3 2 4 5 4 3 4];
 [nlogL, avar] = poisslike (3.25, x);
 assert (nlogL, 13.9533, 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [nlogL, avar] = poisslike (3.25, x, f);
 assert (nlogL, 13.9533, 1e-4)
***** error<poisslike: function called with too few input arguments.> poisslike (1)
***** error<poisslike: LAMBDA must be a positive scalar.> poisslike ([1 2 3], [1 2])
***** error<poisslike: X must be a vector of non-negative values.> ...
 poisslike (3.25, ones (10, 2))
***** error<poisslike: X must be a vector of non-negative values.> ...
 poisslike (3.25, [1 2 3 -4 5])
***** error<poisslike: X and FREQ vectors mismatch.> ...
 poisslike (3.25, ones (10, 1), ones (8,1))
***** error<poisslike: FREQ must not contain negative values.> ...
 poisslike (3.25, ones (1, 8), [1 1 1 1 1 1 1 -1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gevfit.m
***** demo
 ## Sample 2 populations from 2 different exponential distibutions
 rand ("seed", 1);   # for reproducibility
 r1 = gevrnd (-0.5, 1, 2, 5000, 1);
 rand ("seed", 2);   # for reproducibility
 r2 = gevrnd (0, 1, -4, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 50, 5);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their k, sigma, and mu parameters
 k_sigma_muA = gevfit (r(:,1));
 k_sigma_muB = gevfit (r(:,2));

 ## Plot their estimated PDFs
 x = [-10:0.5:20];
 y = gevpdf (x, k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3));
 plot (x, y, "-pr");
 y = gevpdf (x, k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3));
 plot (x, y, "-sg");
 ylim ([0, 0.7])
 xlim ([-7, 5])
 legend ({"Normalized HIST of sample 1 with k=-0.5, σ=1, μ=2", ...
          "Normalized HIST of sample 2 with k=0, σ=1, μ=-4",
     sprintf("PDF for sample 1 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
                 k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3)), ...
     sprintf("PDF for sample 3 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
                 k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3))})
 title ("Two population samples from different exponential distibutions")
 hold off
***** test
 x = 1:50;
 [pfit, pci] = gevfit (x);
 pfit_out = [-0.4407, 15.1923, 21.5309];
 pci_out = [-0.7532, 11.5878, 16.5686; -0.1282, 19.9183, 26.4926];
 assert (pfit, pfit_out, 1e-3);
 assert (pci, pci_out, 1e-3);
***** test
 x = 1:2:50;
 [pfit, pci] = gevfit (x);
 pfit_out = [-0.4434, 15.2024, 21.0532];
 pci_out = [-0.8904, 10.3439, 14.0168; 0.0035, 22.3429, 28.0896];
 assert (pfit, pfit_out, 1e-3);
 assert (pci, pci_out, 1e-3);
***** error<gevfit: X must be a vector.> gevfit (ones (2,5));
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 1.2);
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 0);
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], "alpha");
***** error<gevfit: X and FREQ vectors mismatch.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
***** error<gevfit: FREQ must not contain negative values.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
***** error<gevfit: FREQ must contain integer values.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
***** error<gevfit: 'options' argument must be a structure> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
***** error<gevfit: 'options' argument must be a structure> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/unifit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/unifit.m
***** demo
 ## Sample 2 populations from different continuous uniform distibutions
 rand ("seed", 5);    # for reproducibility
 r1 = unifrnd (2, 5, 2000, 1);
 rand ("seed", 6);    # for reproducibility
 r2 = unifrnd (3, 9, 2000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:10, 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 a_bA = unifit (r(:,1));
 a_bB = unifit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:10];
 y = unifpdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pg");
 y = unifpdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sc");
 xlim ([1, 10])
 ylim ([0, 0.5])
 legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
          "Normalized HIST of sample 2 with a=3 and b=9", ...
          sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
                  a_bB(1), a_bB(2))})
 title ("Two population samples from different continuous uniform distibutions")
 hold off
***** test
 x = 0:5;
 [paramhat, paramci] = unifit (x);
 assert (paramhat, [0, 5]);
 assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
***** test
 x = 0:5;
 [paramhat, paramci] = unifit (x, [], [1 1 1 1 1 1]);
 assert (paramhat, [0, 5]);
 assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
***** assert (unifit ([1 1 2 3]), unifit ([1 2 3], [] ,[2 1 1]))
***** error<unifit: function called with too few input arguments.> unifit ()
***** error<unifit: X cannot have negative values.> unifit (-1, [1 2 3 3])
***** error<unifit: wrong value for ALPHA.> unifit (1, 0)
***** error<unifit: wrong value for ALPHA.> unifit (1, 1.2)
***** error<unifit: wrong value for ALPHA.> unifit (1, [0.02 0.05])
***** error<unifit: X and FREQ vector mismatch.> ...
 unifit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<unifit: FREQ cannot have negative values.> ...
 unifit ([1.5, 0.2], [], [1, -1])
***** error<unifit: X and FREQ vector mismatch.> ...
 unifit ([1.5, 0.2], [], [1, 1, 1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevfit_lmom.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gevfit_lmom.m
***** xtest <31070>
 data = 1:50;
 [pfit, pci] = gevfit_lmom (data);
 expected_p = [-0.28 15.01 20.22]';
 assert (pfit, expected_p, 0.1);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/dist_fit/logilike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/logilike.m
***** test
 nlogL = logilike ([25.5, 8.7725], [1:50]);
 assert (nlogL, 206.6769, 1e-4);
***** test
 nlogL = logilike ([3, 0.8645], [1:5]);
 assert (nlogL, 9.0699, 1e-4);
***** error<logilike: function called with too few input arguments.> logilike (3.25)
***** error<logilike: X must be a vector.> logilike ([5, 0.2], ones (2))
***** error<logilike: PARAMS must be a two-element vector.> ...
 logilike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<logilike: X and CENSOR vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<logilike: X and FREQ vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<logilike: X and FREQ vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/evfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/evfit.m
***** demo
 ## Sample 3 populations from different extreme value distibutions
 rand ("seed", 1);    # for reproducibility
 r1 = evrnd (2, 5, 400, 1);
 rand ("seed", 12);    # for reproducibility
 r2 = evrnd (-5, 3, 400, 1);
 rand ("seed", 13);    # for reproducibility
 r3 = evrnd (14, 8, 400, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 25, 0.4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.28])
 xlim ([-30, 30]);
 hold on

 ## Estimate their MU and SIGMA parameters
 mu_sigmaA = evfit (r(:,1));
 mu_sigmaB = evfit (r(:,2));
 mu_sigmaC = evfit (r(:,3));

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
          "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
          "Normalized HIST of sample 3 with μ=14 and σ=8", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaC(1), mu_sigmaC(2))})
 title ("Three population samples from different extreme value distibutions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = evfit (x);
 paramhat_out = [32.6811, 13.0509];
 paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = evfit (x, 0.01);
 paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
 assert (paramci, paramci_out, 1e-4);
***** error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
***** error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
***** error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
***** error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
***** error<evfit: X and FREQ vectors mismatch.>
 evfit ([1 2 3], 0.05, [], [1 5])
***** error<evfit: FREQ must not contain negative values.>
 evfit ([1 2 3], 0.05, [], [1 5 -1])
***** error<evfit: 'options' 5th argument must be a structure> ...
 evfit ([1:10], 0.05, [], [], 5)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/geofit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/geofit.m
***** demo
 ## Sample 2 populations from different geometric distibutions
 rande ("seed", 1);    # for reproducibility
 r1 = geornd (0.15, 1000, 1);
 rande ("seed", 2);    # for reproducibility
 r2 = geornd (0.5, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:20.5, 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 pshatA = geofit (r(:,1));
 pshatB = geofit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:15];
 y = geopdf (x, pshatA);
 plot (x, y, "-pg");
 y = geopdf (x, pshatB);
 plot (x, y, "-sc");
 xlim ([0, 15])
 ylim ([0, 0.6])
 legend ({"Normalized HIST of sample 1 with ps=0.15", ...
          "Normalized HIST of sample 2 with ps=0.50", ...
          sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
                  mean (pshatA)), ...
          sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
                  mean (pshatB))})
 title ("Two population samples from different geometric distibutions")
 hold off
***** test
 x = 0:5;
 [pshat, psci] = geofit (x);
 assert (pshat, 0.2857, 1e-4);
 assert (psci, [0.092499; 0.478929], 1e-5);
***** test
 x = 0:5;
 [pshat, psci] = geofit (x, [], [1 1 1 1 1 1]);
 assert (pshat, 0.2857, 1e-4);
 assert (psci, [0.092499; 0.478929], 1e-5);
***** assert (geofit ([1 1 2 3]), geofit ([1 2 3], [] ,[2 1 1]))
***** error<geofit: function called with too few input arguments.> geofit ()
***** error<geofit: X cannot have negative values.> geofit (-1, [1 2 3 3])
***** error<geofit: wrong value for ALPHA.> geofit (1, 0)
***** error<geofit: wrong value for ALPHA.> geofit (1, 1.2)
***** error<geofit: wrong value for ALPHA.> geofit (1, [0.02 0.05])
***** error<geofit: X and FREQ vector mismatch.> ...
 geofit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<geofit: X and FREQ vector mismatch.> ...
 geofit ([1.5, 0.2], [], [1, 1, 1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/nakafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/nakafit.m
***** demo
 ## Sample 3 populations from different Nakagami distibutions
 randg ("seed", 5)  # for reproducibility
 r1 = nakarnd (0.5, 1, 2000, 1);
 randg ("seed", 2)   # for reproducibility
 r2 = nakarnd (5, 1, 2000, 1);
 randg ("seed", 7)   # for reproducibility
 r3 = nakarnd (2, 2, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.05:0.1:3.5], 10);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 2.5]);
 xlim ([0, 3.0]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_omegaA = nakafit (r(:,1));
 mu_omegaB = nakafit (r(:,2));
 mu_omegaC = nakafit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.1:3.01];
 y = nakapdf (x, mu_omegaA(1), mu_omegaA(2));
 plot (x, y, "-pr");
 y = nakapdf (x, mu_omegaB(1), mu_omegaB(2));
 plot (x, y, "-sg");
 y = nakapdf (x, mu_omegaC(1), mu_omegaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=0.5 and ω=1", ...
          "Normalized HIST of sample 2 with μ=5 and ω=1", ...
          "Normalized HIST of sample 3 with μ=2 and ω=2", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaA(1), mu_omegaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaB(1), mu_omegaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaC(1), mu_omegaC(2))})
 title ("Three population samples from different Nakagami distibutions")
 hold off
***** test
 paramhat = nakafit ([1:50]);
 paramhat_out = [0.7355, 858.5];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:5]);
 paramhat_out = [1.1740, 11];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [1.1740, 11];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = nakafit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<nakafit: X must be a vector.> nakafit (ones (2,5));
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 1.2);
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 0);
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], "alpha");
***** error<nakafit: X and CENSOR vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<nakafit: X and CENSOR vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<nakafit: X and FREQ vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<nakafit: X and FREQ vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<nakafit: FREQ must not contain negative values.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 -1 1 1]);
***** error<nakafit: FREQ must contain integer values.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 1.5 1 1]);
***** error<nakafit: 'options' 5th argument must be a structure> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/nakalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/nakalike.m
***** test
 nlogL = nakalike ([0.735504, 858.5], [1:50]);
 assert (nlogL, 202.8689, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:5]);
 assert (nlogL, 8.6976, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:5], [], [1, 1, 1, 1, 1]);
 assert (nlogL, 8.6976, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:6], [], [1, 1, 1, 1, 1, 0]);
 assert (nlogL, 8.6976, 1e-4);
***** error<nakalike: function called with too few input arguments.> nakalike (3.25)
***** error<nakalike: X must be a vector.> nakalike ([5, 0.2], ones (2))
***** error<nakalike: PARAMS must be a two-element vector.> ...
 nakalike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<nakalike: X and CENSOR vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<nakalike: X and FREQ vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<nakalike: X and FREQ vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
***** error<nakalike: FREQ must not contain negative values.> ...
 nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1, 1, -1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/loglfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/loglfit.m
***** demo
 ## Sample 3 populations from different log-logistic distibutions
 rand ("seed", 5)  # for reproducibility
 r1 = loglrnd (0, 1, 2000, 1);
 rand ("seed", 2)   # for reproducibility
 r2 = loglrnd (0, 0.5, 2000, 1);
 rand ("seed", 7)   # for reproducibility
 r3 = loglrnd (0, 0.125, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.05:0.1:2.5], 10);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3.5]);
 xlim ([0, 2.0]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 a_bA = loglfit (r(:,1));
 a_bB = loglfit (r(:,2));
 a_bC = loglfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.1:2.01];
 y = loglpdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pr");
 y = loglpdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sg");
 y = loglpdf (x, a_bC(1), a_bC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with α=1 and β=1", ...
          "Normalized HIST of sample 2 with α=1 and β=2", ...
          "Normalized HIST of sample 3 with α=1 and β=8", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_bB(1), a_bB(2)), ...
          sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
                  a_bC(1), a_bC(2))})
 title ("Three population samples from different log-logistic distibutions")
 hold off
***** test
 [paramhat, paramci] = loglfit ([1:50]);
 paramhat_out = [3.09717, 0.468525];
 paramci_out = [2.87261, 0.370616; 3.32174, 0.5923];
 assert (paramhat, paramhat_out, 1e-5);
 assert (paramci, paramci_out, 1e-5);
***** test
 paramhat = loglfit ([1:5]);
 paramhat_out = [1.01124, 0.336449];
 assert (paramhat, paramhat_out, 1e-5);
***** test
 paramhat = loglfit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [1.01124, 0.336449];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = loglfit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = loglfit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<loglfit: X must be a vector.> loglfit (ones (2,5));
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 1.2);
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 0);
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], "alpha");
***** error<loglfit: X and CENSOR vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<loglfit: X and CENSOR vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<loglfit: X and FREQ vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<loglfit: X and FREQ vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<loglfit: 'options' 5th argument must be a structure> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/bisafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/bisafit.m
***** demo
 ## Sample 3 populations from different Birnbaum-Saunders distibutions
 rand ("seed", 5);    # for reproducibility
 r1 = bisarnd (1, 0.5, 2000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = bisarnd (2, 0.3, 2000, 1);
 rand ("seed", 7);    # for reproducibility
 r3 = bisarnd (4, 0.5, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 80, 4.2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 1.1]);
 xlim ([0, 8]);
 hold on

 ## Estimate their α and β parameters
 beta_gammaA = bisafit (r(:,1));
 beta_gammaB = bisafit (r(:,2));
 beta_gammaC = bisafit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:8];
 y = bisapdf (x, beta_gammaA(1), beta_gammaA(2));
 plot (x, y, "-pr");
 y = bisapdf (x, beta_gammaB(1), beta_gammaB(2));
 plot (x, y, "-sg");
 y = bisapdf (x, beta_gammaC(1), beta_gammaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with β=1 and γ=0.5", ...
          "Normalized HIST of sample 2 with β=2 and γ=0.3", ...
          "Normalized HIST of sample 3 with β=4 and γ=0.5", ...
          sprintf("PDF for sample 1 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaA(1), beta_gammaA(2)), ...
          sprintf("PDF for sample 2 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaB(1), beta_gammaB(2)), ...
          sprintf("PDF for sample 3 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaC(1), beta_gammaC(2))})
 title ("Three population samples from different Birnbaum-Saunders distibutions")
 hold off
***** test
 paramhat = bisafit ([1:50]);
 paramhat_out = [16.2649, 1.0156];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = bisafit ([1:5]);
 paramhat_out = [2.5585, 0.5839];
 assert (paramhat, paramhat_out, 1e-4);
***** error<bisafit: X must be a vector.> bisafit (ones (2,5));
***** error<bisafit: X must contain only positive values.> bisafit ([-1 2 3 4]);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 1.2);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 0);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], "alpha");
***** error<bisafit: X and CENSOR vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<bisafit: X and CENSOR vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<bisafit: X and FREQ vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<bisafit: X and FREQ vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<bisafit: 'options' 5th argument must be a structure> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/lognlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/lognlike.m
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.25], x);
 avar_out = [-5.4749e-03, 2.8308e-04; 2.8308e-04, -1.1916e-05];
 assert (nlogL, 3962.330333301793, 1e-10);
 assert (avar, avar_out, 1e-7);
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.25], x * 0.5);
 avar_out = [-7.6229e-03, 4.8722e-04; 4.8722e-04, -2.6754e-05];
 assert (nlogL, 2473.183051225747, 1e-10);
 assert (avar, avar_out, 1e-7);
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.5], x);
 avar_out = [-2.1152e-02, 2.2017e-03; 2.2017e-03, -1.8535e-04];
 assert (nlogL, 1119.072424020455, 1e-12);
 assert (avar, avar_out, 1e-6);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = lognlike ([0, 0.5], x, censor);
 avar_out = [-1.9823e-02, 2.0370e-03; 2.0370e-03, -1.6618e-04];
 assert (nlogL, 1091.746371145497, 1e-12);
 assert (avar, avar_out, 1e-6);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = lognlike ([0, 1], x, censor);
 avar_out = [-6.8634e-02, 1.3968e-02; 1.3968e-02, -2.1664e-03];
 assert (nlogL, 349.3969104144271, 1e-12);
 assert (avar, avar_out, 1e-6);
***** error<lognlike: function called with too few input arguments.> ...
 lognlike ([12, 15]);
***** error<lognlike: X must be a vector.> lognlike ([12, 15], ones (2));
***** error<lognlike: PARAMS must be a two-element vector.> ...
 lognlike ([12, 15, 3], [1:50]);
***** error<lognlike: X and CENSOR vectors mismatch.> ...
 lognlike ([12, 15], [1:50], [1, 2, 3]);
***** error<lognlike: X and FREQ vectors mismatch.> ...
 lognlike ([12, 15], [1:50], [], [1, 2, 3]);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/burrfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/burrfit.m
***** demo
 ## Sample 3 populations from different Burr type XII distibutions
 rand ("seed", 4);    # for reproducibility
 r1 = burrrnd (3.5, 2, 2.5, 10000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = burrrnd (1, 3, 1, 10000, 1);
 rand ("seed", 9);    # for reproducibility
 r3 = burrrnd (0.5, 2, 3, 10000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.2:20], [18, 5, 3]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3]);
 xlim ([0, 5]);
 hold on

 ## Estimate their α and β parameters
 lambda_c_kA = burrfit (r(:,1));
 lambda_c_kB = burrfit (r(:,2));
 lambda_c_kC = burrfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.15:15];
 y = burrpdf (x, lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3));
 plot (x, y, "-pr");
 y = burrpdf (x, lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3));
 plot (x, y, "-sg");
 y = burrpdf (x, lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with λ=3.5, c=2, and k=2.5", ...
          "Normalized HIST of sample 2 with λ=1, c=3, and k=1", ...
          "Normalized HIST of sample 3 with λ=0.5, c=2, and k=3", ...
  sprintf("PDF for sample 1 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3)), ...
  sprintf("PDF for sample 2 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3)), ...
  sprintf("PDF for sample 3 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3))})
 title ("Three population samples from different Burr type XII distibutions")
 hold off
***** test
 l = 1; c = 2; k = 3;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 0.5; c = 1; k = 3;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 1; c = 3; k = 1;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 3; c = 2; k = 1;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 4; c = 2; k = 4;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** error<burrfit: X must be a vector.> burrfit (ones (2,5));
***** error<burrfit: X must contain only positive values.> burrfit ([-1 2 3 4]);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 1.2);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 0);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], "alpha");
***** error<burrfit: X and CENSOR vectors mismatch.> ...
 burrfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<burrfit: X and CENSOR vectors mismatch.> ...
 burrfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<burrfit: X and FREQ vectors mismatch.>
 burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 1, 5])
***** error<burrfit: FREQ must not contain negative values.>
 burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 5, 1, 1, -1])
***** error<burrfit: 'options' 5th argument must be a structure> ...
 burrfit ([1:10], 0.05, [], [], 5)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/hnlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/hnlike.m
***** test
 x = 1:20;
 paramhat = hnfit (x, 0);
 [nlogL, acov] = hnlike (paramhat, x);
 assert (nlogL, 64.179177404891300, 1e-14);
***** test
 x = 1:20;
 paramhat = hnfit (x, 0);
 [nlogL, acov] = hnlike (paramhat, x, ones (1, 20));
 assert (nlogL, 64.179177404891300, 1e-14);
***** error<hnlike: function called with too few input arguments.> ...
 hnlike ([12, 15]);
***** error<hnlike: wrong parameters length.> hnlike ([12, 15, 3], [1:50]);
***** error<hnlike: wrong parameters length.> hnlike ([3], [1:50]);
***** error<hnlike: X must be a vector of real values.> ...
 hnlike ([0, 3], ones (2));
***** error<hnlike: X must be a vector of real values.> ...
 hnlike ([0, 3], [1, 2, 3, 4, 5+i]);
***** error<hnlike: X and FREQ vectors mismatch.> ...
 hnlike ([1, 2], ones (10, 1), ones (8,1))
***** error<hnlike: FREQ must not contain negative values.> ...
 hnlike ([1, 2], ones (1, 8), [1 1 1 1 1 1 1 -1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/wbllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/wbllike.m
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([2.3, 1.2], x);
 avar_out = [0.0250, 0.0062; 0.0062, 0.0017];
 assert (nlogL, 945.9589180651594, 1e-12);
 assert (acov, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([2.3, 1.2], x * 0.5);
 avar_out = [-0.3238, -0.1112; -0.1112, -0.0376];
 assert (nlogL, 424.9879809704742, 6e-14);
 assert (acov, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([21, 15], x);
 avar_out = [-0.00001236, -0.00001166; -0.00001166, -0.00001009];
 assert (nlogL, 1635190.328991511, 1e-8);
 assert (acov, avar_out, 1e-8);
***** error<wbllike: too few input arguments.> wbllike ([12, 15]);
***** error<wbllike: wrong parameters length.> wbllike ([12, 15, 3], [1:50]);
***** error<wbllike: X must be a vector.> wbllike ([12, 3], ones (10, 2));
***** error<wbllike: X and CENSOR> wbllike ([12, 15], [1:50], [1, 2, 3]);
***** error<wbllike: X and FREQ> wbllike ([12, 15], [1:50], [], [1, 2, 3]);
***** error<wbllike: FREQ cannot have negative values.> ...
 wbllike ([12, 15], [1:5], [], [1, 2, 3, -1, 0]);
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/normfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/normfit.m
***** demo
 ## Sample 3 populations from 3 different normal distibutions
 randn ("seed", 1);    # for reproducibility
 r1 = normrnd (2, 5, 5000, 1);
 randn ("seed", 2);    # for reproducibility
 r2 = normrnd (5, 2, 5000, 1);
 randn ("seed", 3);    # for reproducibility
 r3 = normrnd (9, 4, 5000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 15, 0.4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu and sigma parameters
 [muhat, sigmahat] = normfit (r);

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = normpdf (x, muhat(1), sigmahat(1));
 plot (x, y, "-pr");
 y = normpdf (x, muhat(2), sigmahat(2));
 plot (x, y, "-sg");
 y = normpdf (x, muhat(3), sigmahat(3));
 plot (x, y, "-^c");
 ylim ([0, 0.5])
 xlim ([-20, 20])
 hold off
 legend ({"Normalized HIST of sample 1 with mu=2, σ=5", ...
          "Normalized HIST of sample 2 with mu=5, σ=2", ...
          "Normalized HIST of sample 3 with mu=9, σ=4", ...
          sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(1), sigmahat(1)), ...
          sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(2), sigmahat(2)), ...
          sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(3), sigmahat(3))}, "location", "northwest")
 title ("Three population samples from different normal distibutions")
 hold off
***** test
 load lightbulb
 idx = find (lightbulb(:,2) == 0);
 censoring = lightbulb(idx,3) == 1;
 [muHat, sigmaHat] = normfit (lightbulb(idx,1), [], censoring);
 assert (muHat, 9496.59586737857, 1e-11);
 assert (sigmaHat, 3064.021012796456, 2e-12);
***** test
 randn ("seed", 234);
 x = normrnd (3, 5, [1000, 1]);
 [muHat, sigmaHat, muCI, sigmaCI] = normfit (x, 0.01);
 assert (muCI(1) < 3);
 assert (muCI(2) > 3);
 assert (sigmaCI(1) < 5);
 assert (sigmaCI(2) > 5);
***** error<normfit: X must not be a multi-dimensional array.> ...
 normfit (ones (3,3,3))
***** error<normfit: matrix data acceptable only under 2-arg syntax.> ...
 normfit (ones (20,3), [], zeros (20,1))
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), -0.3)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 1.2)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), [0.05 0.1])
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0.02+i)
***** error<normfit: X and CENSOR vectors mismatch.> ...
 normfit (ones (20,1), [], zeros(15,1))
***** error<normfit: X and FREQ vectors mismatch.> ...
 normfit (ones (20,1), [], zeros(20,1), ones(25,1))
***** error<normfit: FREQ must not contain negative values.> ...
 normfit (ones (5,1), [], zeros(5,1), [1, 2, 1, 2, -1]')
***** error<normfit: > normfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/invglike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/invglike.m
***** test
 nlogL = invglike ([25.5, 19.6973], [1:50]);
 assert (nlogL, 219.1516, 1e-4);
***** test
 nlogL = invglike ([3, 8.1081], [1:5]);
 assert (nlogL, 9.0438, 1e-4);
***** error<invglike: function called with too few input arguments.> invglike (3.25)
***** error<invglike: X must be a vector.> invglike ([5, 0.2], ones (2))
***** error<invglike: X must have positive values.> invglike ([5, 0.2], [-1, 3])
***** error<invglike: PARAMS must be a two-element vector.> ...
 invglike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<invglike: X and CENSOR vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<invglike: X and FREQ vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<invglike: X and FREQ vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [], [1, 1, 1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/binolike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/binolike.m
***** assert (binolike ([3, 0.333], [0:3]), 6.8302, 1e-4)
***** assert (binolike ([3, 0.333], 0), 1.2149, 1e-4)
***** assert (binolike ([3, 0.333], 1), 0.8109, 1e-4)
***** assert (binolike ([3, 0.333], 2), 1.5056, 1e-4)
***** assert (binolike ([3, 0.333], 3), 3.2988, 1e-4)
***** test
 [nlogL, acov] = binolike ([3, 0.333], 3);
 assert (acov(4), 0.0740, 1e-4)
***** error<binolike: function called with too few input arguments.> binolike (3.25)
***** error<binolike: X must be a vector.> binolike ([5, 0.2], ones (2))
***** error<binolike: PARAMS must be a two-element vector.> ...
 binolike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<binolike: number of trials,> binolike ([1.5, 0.2], 1)
***** error<binolike: number of trials,> binolike ([-1, 0.2], 1)
***** error<binolike: number of trials,> binolike ([Inf, 0.2], 1)
***** error<binolike: probability of success,> binolike ([5, 1.2], [3, 5])
***** error<binolike: probability of success,> binolike ([5, -0.2], [3, 5])
***** error<binolike: X and FREQ vectors mismatch.> ...
 binolike ([5, 0.5], ones (10, 1), ones (8,1))
***** error<binolike: FREQ must not contain negative values.> ...
 binolike ([5, 0.5], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<binolike: X cannot have negative values.> binolike ([5, 0.2], [-1, 3])
***** error<binolike: number of successes,> binolike ([5, 0.2], [3, 5, 7])
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/fitcsvm.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcsvm.m
***** demo
 ## Use a subset of Fisher's iris data set

 load fisheriris
 inds = ! strcmp (species, 'setosa');
 X = meas(inds, [3,4]);
 Y = species(inds);

 ## Train a linear SVM classifier
 SVMModel = fitcsvm (X, Y)

 ## Plot a scatter diagram of the data and circle the support vectors.
 sv = SVMModel.SupportVectors;
 figure
 gscatter (X(:,1), X(:,2), Y)
 hold on
 plot (sv(:,1), sv(:,2), 'ko', 'MarkerSize', 10)
 legend ('versicolor', 'virginica', 'Support Vector')
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcsvm (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y}, {x, y})
 assert (a.NumObservations, 4)
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert (a.ModelParameters.SVMtype, "c_svc")
 assert (a.ClassNames, {"a"; "b"})
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.BoxConstraint, 1)
 assert (a.ModelParameters.KernelOffset, 0)
 assert (a.ClassNames, [1; -1])
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
 "KernelOffset", 2);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
 assert (a.ModelParameters.BoxConstraint, 2)
 assert (a.ModelParameters.KernelOffset, 2)
 assert (isempty (a.Alpha), true)
 assert (isempty (a.Beta), false)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "polynomial", "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Alpha), true)
 assert (isempty (a.Beta), false)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "linear", "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Alpha), false)
 assert (isempty (a.Beta), true)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "linear", "CrossVal", 'on');
 assert (class (a), "ClassificationPartitionedModel");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Trained{1}.Alpha), false)
 assert (isempty (a.Trained{1}.Beta), true)
***** error<fitcsvm: too few arguments.> fitcsvm ()
***** error<fitcsvm: too few arguments.> fitcsvm (ones (4,1))
***** error<fitcsvm: Name-Value arguments must be in pairs.>
 fitcsvm (ones (4,2), ones (4, 1), 'KFold')
***** error<fitcsvm: number of rows in X and Y must be equal.>
 fitcsvm (ones (4,2), ones (3, 1))
***** error<fitcsvm: number of rows in X and Y must be equal.>
 fitcsvm (ones (4,2), ones (3, 1), 'KFold', 2)
***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
 fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 2)
***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
 fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 'a')
***** error <fitcsvm: You can use only one cross-validation name-value pair argument> ...
 fitcsvm (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/bar3.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/bar3.m
***** demo
 ## Ploting 5 bars in the same series.

 z = [50; 40; 30; 20; 10];
 bar3 (z);
***** demo
 ## Ploting 5 bars in different groups.

 z = [50, 40, 30, 20, 10];
 bar3 (z);
***** demo
 ## A 3D bar graph with each series corresponding to a column in z.

 z = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
 bar3 (z);
***** demo
 ## Specify y-axis locations as tick names. y must be a column vector!

 y = [1950, 1960, 1970, 1980, 1990]';
 z = [16, 8, 4, 2, 1]';
 bar3 (y, z);
***** demo
 ## Plot 3 series as a grouped plot without any space between the grouped bars

 z = [70 50 33 10; 75 55 35 15; 80 60 40 20];
 bar3 (z, 1, 'grouped');
***** demo
 ## Plot a stacked style 3D bar graph

 z = [19, 30, 21, 30; 40, 16, 32, 12];
 b = bar3 (z, 0.5, 'stacked');
***** error <bar3: Z must be numeric.> bar3 ("A")
***** error <bar3: Z must be numeric.> bar3 ({2,3,4,5})
***** error <bar3: inconsistent size in Y and Z input arguments.> ...
 bar3 ([1,2,3]', ones (2))
***** error <bar3: WIDTH must be a scalar in the range> ...
 bar3 ([1:5], 1.2)
***** error <bar3: WIDTH must be a scalar in the range> ...
 bar3 ([1:5]', ones (5), 1.2)
***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3 ([1:5]', ones (5), [0.8, 0.7])
***** error <bar3: missing value for optional argument 'width'.> ...
 bar3 (ones (5), 'width')
***** error <bar3: invalid value for optional argument 'width'.> ...
 bar3 (ones (5), 'width', 1.2)
***** error <bar3: invalid value for optional argument 'width'.> ...
 bar3 (ones (5), 'width', [0.8, 0.8, 0.8])
***** error <bar3: missing value for optional argument 'color'.> ...
 bar3 (ones (5), 'color')
***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3 (ones (5), 'color', [0.8, 0.8])
***** error <bar3: invalid value for optional argument 'color'.> ...
 bar3 (ones (5), 'color', "brown")
***** error <bar3: invalid value for optional argument 'color'.> ...
 bar3 (ones (5), 'color', {"r", "k", "c", "m", "brown"})
***** error <bar3: missing value for optional argument 'xlabel'.> ...
 bar3 (ones (5), 'xlabel')
***** error <bar3: invalid value for optional argument 'xlabel'.> ...
 bar3 (ones (5), 'xlabel', 4)
***** error <bar3: missing value for optional argument 'ylabel'.> ...
 bar3 (ones (5), 'ylabel')
***** error <bar3: invalid value for optional argument 'ylabel'.> ...
 bar3 (ones (5), 'ylabel', 4)
***** error <bar3: invalid optional argument.> bar3 (ones (5), 'this', 4)
***** error <bar3: the elements in 'xlabel' must equal the columns in Z.> ...
 bar3 (ones (5), 'xlabel', {"A", "B", "C"})
***** error <bar3: the elements in 'ylabel' must equal the rows in Z.> ...
 bar3 (ones (5), 'ylabel', {"A", "B", "C"})
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/mcnemar_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/mcnemar_test.m
***** test
 [h, pval, chisq] = mcnemar_test ([101,121;59,33]);
 assert (h, 1);
 assert (pval, 3.8151e-06, 1e-10);
 assert (chisq, 21.356, 1e-3);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80]);
 assert (h, 1);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.01);
 assert (h, 0);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "mid-p");
 assert (h, 1);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "asymptotic");
 assert (h, 1);
 assert (pval, 0.033006, 1e-6);
 assert (chisq, 4.5455, 1e-4);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "exact");
 assert (h, 0);
 assert (pval, 0.052479, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "corrected");
 assert (h, 0);
 assert (pval, 0.055009, 1e-6);
 assert (chisq, 3.6818, 1e-4);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.1, "corrected");
 assert (h, 1);
 assert (pval, 0.055009, 1e-6);
 assert (chisq, 3.6818, 1e-4);
***** error<mcnemar_test: too many input arguments.> mcnemar_test (59, 6, 16, 80)
***** error<mcnemar_test: X must be a 2x2 matrix.> mcnemar_test (ones (3, 3))
***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
 mcnemar_test ([59,6;16,-80])
***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
 mcnemar_test ([59,6;16,4.5])
***** error<mcnemar_test: invalid 2nd input argument.> ...
 mcnemar_test ([59,6;16,80], {""})
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], -0.2)
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], [0.05, 0.1])
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], 1)
***** error<mcnemar_test: invalid value for TESTTYPE.> ...
 mcnemar_test ([59,6;16,80], "")
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/fitcgam.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcgam.m
***** demo
 ## Train a GAM classifier for binary classification
 ## using specific data and plot the decision boundaries.

 ## Define specific data
 X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
     6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
 Y = [0; 0; 0; 0; 0; ...
     1; 1; 1; 1; 1];

 ## Train the GAM model
 obj = fitcgam (X, Y, "Interactions", "all");

 ## Create a grid of values for prediction
 x1 = [min(X(:,1)):0.1:max(X(:,1))];
 x2 = [min(X(:,2)):0.1:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);

 ## Plot decision boundaries and data points
 predNumeric = str2double (pred);
 gidx = predNumeric > 0.5;

 figure
 scatter(XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter(XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot(X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel("Feature 1");
 ylabel("Feature 2");
 title("Generalized Additive Model (GAM) Decision Boundary");
 legend({"Class 1 Region", "Class 0 Region", ...
       "Class 1 Samples", "Class 0 Samples"}, ...
       "location", "northwest")
 axis tight
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 a = fitcgam (x, y, "PredictorNames", PredictorNames);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, PredictorNames)
 assert (a.BaseModel.Intercept, 0)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 a = fitcgam (x, y, "interactions", "all");
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
 assert (a.ClassNames, {'1'; '0'})
 assert (a.PredictorNames, {'x1', 'x2'})
 assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 a = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
 assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (a.ModelwInt.Intercept, 0)
***** error<fitcgam: too few arguments.> fitcgam ()
***** error<fitcgam: too few arguments.> fitcgam (ones (4,1))
***** error<fitcgam: name-value arguments must be in pairs.>
 fitcgam (ones (4,2), ones (4, 1), "K")
***** error<fitcgam: number of rows in X and Y must be equal.>
 fitcgam (ones (4,2), ones (3, 1))
***** error<fitcgam: number of rows in X and Y must be equal.>
 fitcgam (ones (4,2), ones (3, 1), "K", 2)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/hotelling_t2test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hotelling_t2test.m
***** error<Invalid call to hotelling_t2test.  Correct usage> hotelling_t2test ();
***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
 hotelling_t2test (1);
***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
 hotelling_t2test (ones(2,2,2));
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", 1);
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", -0.2);
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", "a");
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", [0.01, 0.05]);
***** error<hotelling_t2test: invalid Name argument.> ...
 hotelling_t2test (ones(20,2), [0, 0], "name", 0.01);
***** error<hotelling_t2test: if X is a vector, M must be a scalar.> ...
 hotelling_t2test (ones(20,1), [0, 0]);
***** error<hotelling_t2test: X must have more rows than columns.> ...
 hotelling_t2test (ones(4,5), [0, 0, 0, 0, 0]);
***** error<hotelling_t2test: if X is a matrix, M must be a vector of length> ...
 hotelling_t2test (ones(20,5), [0, 0, 0, 0]);
***** test
 randn ("seed", 1);
 x = randn (50000, 5);
 [h, pval, stats] = hotelling_t2test (x);
 assert (h, 0);
 assert (stats.df1, 5);
 assert (stats.df2, 49995);
***** test
 randn ("seed", 1);
 x = randn (50000, 5);
 [h, pval, stats] = hotelling_t2test (x, ones (1, 5) * 10);
 assert (h, 1);
 assert (stats.df1, 5);
 assert (stats.df2, 49995);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/cmdscale.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cmdscale.m
***** shared m, n, X, D
 m = randi(100) + 1; n = randi(100) + 1; X = rand(m, n); D = pdist(X);
***** assert(norm(pdist(cmdscale(D))), norm(D), sqrt(eps))
***** assert(norm(pdist(cmdscale(squareform(D)))), norm(D), sqrt(eps))
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/geomean.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/geomean.m
***** test
 x = [0:10];
 y = [x;x+5;x+10];
 assert (geomean (x), 0);
 m = [0 9.462942809849169 14.65658770861967];
 assert (geomean (y, 2), m', 4e-14);
 assert (geomean (y, "all"), 0);
 y(2,4) = NaN;
 m(2) = 9.623207231679554;
 assert (geomean (y, 2), [0 NaN m(3)]', 4e-14);
 assert (geomean (y', "omitnan"), m, 4e-14);
 z = y + 20;
 assert (geomean (z, "all"), NaN);
 assert (geomean (z, "all", "includenan"), NaN);
 assert (geomean (z, "all", "omitnan"), 29.59298474535024, 4e-14);
 m = [24.79790781765634 NaN 34.85638839503932];
 assert (geomean (z'), m, 4e-14);
 assert (geomean (z', "includenan"), m, 4e-14);
 m(2) = 30.02181156156319;
 assert (geomean (z', "omitnan"), m, 4e-14);
 assert (geomean (z, 2, "omitnan"), m', 4e-14);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (geomean (x, [3 2])), [10 1 1 3]);
 assert (size (geomean (x, [1 2])), [1 1 6 3]);
 assert (size (geomean (x, [1 2 4])), [1 1 6]);
 assert (size (geomean (x, [1 4 3])), [1 40]);
 assert (size (geomean (x, [1 2 3 4])), [1 1]);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 m = repmat ([8.304361203739333;14.3078118884256], [5 1 1 3]);
 assert (geomean (x, [3 2]), m, 4e-13);
 x(2,5,6,3) = NaN;
 m(2,3) = NaN;
 assert (geomean (x, [3 2]), m, 4e-13);
 m(2,3) = 14.3292729579901;
 assert (geomean (x, [3 2], "omitnan"), m, 4e-13);
***** error <geomean: X must contain real nonnegative values.> geomean ("char")
***** error <geomean: X must contain real nonnegative values.> geomean ([1 -1 3])
***** error <geomean: DIM must be a positive integer scalar or vector.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
***** error <geomean: DIM must be a positive integer scalar or vector.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
***** error <geomean: VECDIM must contain non-repeating positive integers.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/glmfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/glmfit.m
***** demo
 rand ("seed", 1);
 X = rand (100, 1);
 b_true = [0.5; -1.2];
 mu = exp (b_true(1) + b_true(2) * X);
 randp ("seed", 1);
 y = poissrnd (mu);
 ## Fit a GLM model using the poisson distribution
 [b,dev] = glmfit (X, y, 'poisson');
***** demo
 x = [2100 2300 2500 2700 2900 3100 3300 3500 3700 3900 4100 4300]';
 n = [48 42 31 34 31 21 23 23 21 16 17 21]';
 y = [1 2 0 3 8 8 14 17 19 15 17 21]';
 [b,dev] = glmfit (x,[y n],'binomial','Link','probit');
***** test
 rand ("seed", 1);
 X = rand (50, 1);
 b_true = [0.4; 1.5];
 mu_true = exp (b_true(1) + b_true(2) * X);
 randp ("seed", 1);
 y = poissrnd (mu_true);
 b = glmfit (X, y, "poisson", "link", "log");
 assert (b(1), b_true(1), 0.5);
 assert (b(2), b_true(2), 0.5);
***** test
 rand ("seed", 1);
 X1 = rand (50, 1);
 X2 = rand (50, 1) * 0.5;
 b_true = [0.4; 1.5; -0.7];
 mu_true = exp (b_true(1) + b_true(2) * X1 + b_true(3) * X2);
 randp ("seed", 1);
 y = poissrnd(mu_true);
 [b, dev] = glmfit ([X1, X2], y, "poisson", "link", "log");
 assert (b(1), b_true(1), 1);
 assert (b(2), b_true(2), 1);
 assert (b(3), b_true(3), 1);
 assert (dev < 60, true);
***** error <glmfit: too few input arguments.> glmfit ()
***** error <glmfit: too few input arguments.> glmfit (1)
***** error <glmfit: too few input arguments.> glmfit (1, 2)
***** error <glmfit: Name-Value arguments must be in pairs.> ...
 glmfit (rand (6, 1), rand (6, 1), 'poisson', 'link')
***** error <glmfit: X must be a numeric.> ...
 glmfit ('abc', rand (6, 1), 'poisson')
***** error <glmfit: Y must be a numeric.> ...
 glmfit (rand (5, 2), 'abc', 'poisson')
***** error <glmfit: X and Y must have the same number of observations.> ...
 glmfit (rand (5, 2), rand (6, 1), 'poisson')
***** error <glmfit: DISTRIBUTION must be a character vector.> ...
 glmfit (rand (6, 2), rand (6, 1), 3)
***** error <glmfit: DISTRIBUTION must be a character vector.> ...
 glmfit (rand (6, 2), rand (6, 1), {'poisson'})
***** error <glmfit: for a binomial distribution, Y must be an n-by-1 or n-by-2 matrix.> ...
 glmfit (rand (5, 2), rand (5, 3), 'binomial')
***** error <glmfit: for distributions other than the binomial, Y must be an n-by-1 column vector> ...
 glmfit (rand (5, 2), rand (5, 2), 'normal')
***** error <glmfit: 'gamma' distribution is not supported yet.> ...
 glmfit (rand (5, 2), rand (5, 1), 'gamma')
***** error <glmfit: 'inverse gaussian' distribution is not supported yet.> ...
 glmfit (rand (5, 2), rand (5, 1), 'inverse gaussian')
***** error <glmfit: unknown distribution.> ...
 glmfit (rand (5, 2), rand (5, 1), 'loguniform')
***** error <glmfit: custom link functions must be in a three-element cell array.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log'})
***** error <glmfit: custom link functions must be in a three-element cell array.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log', 'hijy'})
***** error <glmfit: custom link functions must be function handles.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log','dfv','dfgvd'})
***** error <glmfit: custom link functions must be function handles.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@log, 'derivative', @exp})
***** error <glmfit: custom inverse link function must return output of the same size as input.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@exp, @log, @(x) eye(e)})
***** error <glmfit: unsupported link function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'somelinkfunction')
***** error <glmfit: invalid value for link function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 2)
***** error <glmfit: constant should be either 'on' or 'off'.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'log', 'constant', 0)
***** error <glmfit: constant should be either 'on' or 'off'.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'log', 'constant', 'asda')
***** error <glmfit: unknown parameter name.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'param', 'log', 'constant', 'on')
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/x2fx.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/x2fx.m
***** test
 X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
 D = x2fx(X,'quadratic');
 assert (D(1,:) , [1, 1, 10, 10, 1, 100]);
 assert (D(2,:) , [1, 2, 20, 40, 4, 400]);
***** test
 X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
 model = [0, 0; 1, 0; 0, 1; 1, 1; 2, 0];
 D = x2fx(X,model);
 assert (D(1,:) , [1, 1, 10, 10, 1]);
 assert (D(2,:) , [1, 2, 20, 40, 4]);
 assert (D(4,:) , [1, 4, 20, 80, 16]);
***** error x2fx ([1, 10; 2, 20; 3, 10], [0; 1]);
***** error x2fx ([1, 10, 15; 2, 20, 40; 3, 10, 25], [0, 0; 1, 0; 0, 1; 1, 1; 2, 0]);
***** error x2fx ([1, 10; 2, 20; 3, 10], "whatever");
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/kruskalwallis.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/kruskalwallis.m
***** demo
 x = meshgrid (1:6);
 x = x + normrnd (0, 1, 6, 6);
 kruskalwallis (x, [], 'off');
***** demo
 x = meshgrid (1:6);
 x = x + normrnd (0, 1, 6, 6);
 [p, atab] = kruskalwallis(x);
***** demo
 x = ones (30, 4) .* [-2, 0, 1, 5];
 x = x + normrnd (0, 2, 30, 4);
 group = {"A", "B", "C", "D"};
 kruskalwallis (x, group);
***** test
 data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
         0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
         0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
         1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
         0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
         1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
         0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
         0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
         1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
         0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
 group = [1:10] .* ones (10,10);
 group = group(:);
 [p, tbl] = kruskalwallis (data, group, "off");
 assert (p, 0.048229, 1e-6);
 assert (tbl{2,5}, 17.03124, 1e-5);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 82655.5, 1e-16);
 data = reshape (data, 10, 10);
 [p, tbl, stats] = kruskalwallis (data, [], "off");
 assert (p, 0.048229, 1e-6);
 assert (tbl{2,5}, 17.03124, 1e-5);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 82655.5, 1e-16);
 means = [51.85, 60.45, 37.6, 51.1, 29.5, 54.25, 64.55, 66.7, 53.65, 35.35];
 N = 10 * ones (1, 10);
 assert (stats.meanranks, means, 1e-6);
 assert (length (stats.gnames), 10, 0);
 assert (stats.n, N, 0);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/kmeans.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/kmeans.m
***** demo
 ## Generate a two-cluster problem
 randn ("seed", 31)  # for reproducibility
 C1 = randn (100, 2) + 1;
 randn ("seed", 32)  # for reproducibility
 C2 = randn (100, 2) - 1;
 data = [C1; C2];

 ## Perform clustering
 rand ("seed", 1)  # for reproducibility
 [idx, centers] = kmeans (data, 2);

 ## Plot the result
 figure;
 plot (data (idx==1, 1), data (idx==1, 2), "ro");
 hold on;
 plot (data (idx==2, 1), data (idx==2, 2), "bs");
 plot (centers (:, 1), centers (:, 2), "kv", "markersize", 10);
 hold off;
***** demo
 ## Cluster data using k-means clustering, then plot the cluster regions
 ## Load Fisher's iris data set and use the petal lengths and widths as
 ## predictors

 load fisheriris
 X = meas(:,3:4);

 figure;
 plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
 title ("Fisher's Iris Data");
 xlabel ("Petal Lengths (cm)");
 ylabel ("Petal Widths (cm)");

 ## Cluster the data. Specify k = 3 clusters
 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 3);
 x1 = min (X(:,1)):0.01:max (X(:,1));
 x2 = min (X(:,2)):0.01:max (X(:,2));
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];

 idx2Region = kmeans (XGrid, 3, "MaxIter", 1, "Start", C);
 figure;
 gscatter (XGrid(:,1), XGrid(:,2), idx2Region, ...
           [0, 0.75, 0.75; 0.75, 0, 0.75; 0.75, 0.75, 0], "..");
 hold on;
 plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
 title ("Fisher's Iris Data");
 xlabel ("Petal Lengths (cm)");
 ylabel ("Petal Widths (cm)");
 legend ("Region 1", "Region 2", "Region 3", "Data", "Location", "SouthEast");
 hold off
***** demo
 ## Partition Data into Two Clusters

 randn ("seed", 1)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 2)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 X = [r1; r2];

 figure;
 plot (X(:,1), X(:,2), ".");
 title ("Randomly Generated Data");
 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 2, "Distance", "cityblock", ...
                          "Replicates", 5, "Display", "final");
 figure;
 plot (X(idx==1,1), X(idx==1,2), "r.", "MarkerSize", 12);
 hold on
 plot(X(idx==2,1), X(idx==2,2), "b.", "MarkerSize", 12);
 plot (C(:,1), C(:,2), "kx", "MarkerSize", 15, "LineWidth", 3);
 legend ("Cluster 1", "Cluster 2", "Centroids", "Location", "NorthWest");
 title ("Cluster Assignments and Centroids");
 hold off
***** demo
 ## Assign New Data to Existing Clusters

 ## Generate a training data set using three distributions
 randn ("seed", 5)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 7)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 randn ("seed", 9)  # for reproducibility
 r3 = randn (100, 2) * 0.75;
 X = [r1; r2; r3];

 ## Partition the training data into three clusters by using kmeans

 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 3);

 ## Plot the clusters and the cluster centroids

 figure
 gscatter (X(:,1), X(:,2), idx, "bgm", "***");
 hold on
 plot (C(:,1), C(:,2), "kx");
 legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid")

 ## Generate a test data set
 randn ("seed", 25)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 27)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 randn ("seed", 29)  # for reproducibility
 r3 = randn (100, 2) * 0.75;
 Xtest = [r1; r2; r3];

 ## Classify the test data set using the existing clusters
 ## Find the nearest centroid from each test data point by using pdist2

 D = pdist2 (C, Xtest, "euclidean");
 [group, ~] = find (D == min (D));

 ## Plot the test data and label the test data using idx_test with gscatter

 gscatter (Xtest(:,1), Xtest(:,2), group, "bgm", "ooo");
 legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid", ...
         "Data classified to Cluster 1", "Data classified to Cluster 2", ...
         "Data classified to Cluster 3", "Location", "NorthWest");
 title ("Assign New Data to Existing Clusters");
***** test
 samples = 4;
 dims = 3;
 k = 2;
 [cls, c, d, z] = kmeans (rand (samples,dims), k, "start", rand (k,dims, 5),
                          "emptyAction", "singleton");
 assert (size (cls), [samples, 1]);
 assert (size (c), [k, dims]);
 assert (size (d), [k, 1]);
 assert (size (z), [samples, k]);
***** test
 samples = 4;
 dims = 3;
 k = 2;
 [cls, c, d, z] = kmeans (rand (samples,dims), [], "start", rand (k,dims, 5),
                          "emptyAction", "singleton");
 assert (size (cls), [samples, 1]);
 assert (size (c), [k, dims]);
 assert (size (d), [k, 1]);
 assert (size (z), [samples, k]);
***** test
 [cls, c] = kmeans ([1 0; 2 0], 2, "start", [8,0;0,8], "emptyaction", "drop");
 assert (cls, [1; 1]);
 assert (c, [1.5, 0; NA, NA]);
***** test
 kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 5,
         "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "sample", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "plus", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "cluster", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "uniform", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "sqeuclidean", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "cityblock", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "cosine", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "correlation", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "hamming", "emptyAction", "singleton");
***** test
 kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "singleton");
***** error kmeans (rand (3,2), 4);
***** error kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "panic");
***** error kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 1);
***** error kmeans (rand (4,3), 2, "start", rand (2,2));
***** error kmeans (rand (4,3), 2, "distance", "manhattan");
***** error kmeans (rand (3,4), 2, "start", "normal");
***** error kmeans (rand (4,3), 2, "replicates", i);
***** error kmeans (rand (4,3), 2, "replicates", -1);
***** error kmeans (rand (4,3), 2, "replicates", []);
***** error kmeans (rand (4,3), 2, "replicates", [1 2]);
***** error kmeans (rand (4,3), 2, "replicates", "one");
***** error kmeans (rand (4,3), 2, "MAXITER", i);
***** error kmeans (rand (4,3), 2, "MaxIter", -1);
***** error kmeans (rand (4,3), 2, "maxiter", []);
***** error kmeans (rand (4,3), 2, "maxiter", [1 2]);
***** error kmeans (rand (4,3), 2, "maxiter", "one");
***** error <empty cluster created> kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "error");
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/chi2test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/chi2test.m
***** error chi2test ();
***** error chi2test ([1, 2, 3, 4, 5]);
***** error chi2test ([1, 2; 2, 1+3i]);
***** error chi2test ([NaN, 6; 34, 12]);
***** error<chi2test: optional arguments are not supported for 2-way> ...
 p = chi2test (ones (3, 3), "mutual", []);
***** error<chi2test: invalid model name for testing a 3-way table.> ...
 p = chi2test (ones (3, 3, 3), "testtype", 2);
***** error<chi2test: optional arguments must be in pairs.> ...
 p = chi2test (ones (3, 3, 3), "mutual");
***** error<chi2test: value must be numeric in optional argument> ...
 p = chi2test (ones (3, 3, 3), "joint", ["a"]);
***** error<chi2test: value must be empty or scalar in optional argument> ...
 p = chi2test (ones (3, 3, 3), "joint", [2, 3]);
***** error<chi2test: optional arguments are not supported for k> ...
 p = chi2test (ones (3, 3, 3, 4), "mutual", [])
***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (2));
***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (3, 2));
***** warning<chi2test: Expected values less than 1.> p = chi2test (0.4 * ones (3));
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 p = chi2test (x);
 assert (p, 0.017787, 1e-6);
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 [p, chisq] = chi2test (x);
 assert (chisq, 11.9421, 1e-4);
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 [p, chisq, df] = chi2test (x);
 assert (df, 4);
***** test
***** shared x
 x(:,:,1) = [59, 32; 9,16];
 x(:,:,2) = [55, 24;12,33];
 x(:,:,3) = [107,80;17,56];%!
***** assert (chi2test (x), 2.282063427117009e-11, 1e-14);
***** assert (chi2test (x, "mutual", []), 2.282063427117009e-11, 1e-14);
***** assert (chi2test (x, "joint", 1), 1.164834895206468e-11, 1e-14);
***** assert (chi2test (x, "joint", 2), 7.771350230001417e-11, 1e-14);
***** assert (chi2test (x, "joint", 3), 0.07151361728026107, 1e-14);
***** assert (chi2test (x, "marginal", 1), 0, 1e-14);
***** assert (chi2test (x, "marginal", 2), 6.347555814301131e-11, 1e-14);
***** assert (chi2test (x, "marginal", 3), 0, 1e-14);
***** assert (chi2test (x, "conditional", 1), 0.2303114201312508, 1e-14);
***** assert (chi2test (x, "conditional", 2), 0.0958810684407079, 1e-14);
***** assert (chi2test (x, "conditional", 3), 2.648037344954446e-11, 1e-14);
***** assert (chi2test (x, "homogeneous", []), 0.4485579470993741, 1e-14);
***** test
 [pval, chisq, df, E] = chi2test (x);
 assert (chisq, 64.0982, 1e-4);
 assert (df, 7);
 assert (E(:,:,1), [42.903, 39.921; 17.185, 15.991], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "joint", 2);
 assert (chisq, 56.0943, 1e-4);
 assert (df, 5);
 assert (E(:,:,2), [40.922, 23.310; 38.078, 21.690], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "marginal", 3);
 assert (chisq, 146.6058, 1e-4);
 assert (df, 9);
 assert (E(:,1,1), [61.642; 57.358], ones (2, 1) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "conditional", 3);
 assert (chisq, 52.2509, 1e-4);
 assert (df, 3);
 assert (E(:,:,1), [53.345, 37.655; 14.655, 10.345], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "homogeneous", []);
 assert (chisq, 1.6034, 1e-4);
 assert (df, 2);
 assert (E(:,:,1), [60.827, 31.382; 7.173, 16.618], ones (2, 2) * 1e-3);
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvtcdf.m
***** demo
 ## Compute the cdf of a multivariate Student's t distribution with
 ## correlation parameters rho = [1, 0.4; 0.4, 1] and 2 degrees of freedom.

 rho = [1, 0.4; 0.4, 1];
 df = 2;
 [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
 X = [X1(:), X2(:)];
 p = mvtcdf (X, rho, df);
 surf (X1, X2, reshape (p, 25, 25));
 title ("Bivariate Student's t cummulative distribution function");
***** test
 x = [1, 2];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 a = [-1, 0];
 assert (mvtcdf(a, x, rho, df), 0.294196905339283, 1e-14);
***** test
 x = [1, 2;2, 4;1, 5];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 p =[0.790285178602166; 0.938703291727784; 0.81222737321336];
 assert (mvtcdf(x, rho, df), p, 1e-14);
***** test
 x = [1, 2, 2, 4, 1, 5];
 rho = eye (6);
 rho(rho == 0) = 0.5;
 df = 4;
 assert (mvtcdf(x, rho, df), 0.6874, 1e-4);
***** error mvtcdf (1)
***** error mvtcdf (1, 2)
***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
 mvtcdf (1, [2, 3; 3, 2], 1)
***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
 mvtcdf ([2, 3, 4], ones (2), 1)
***** error<mvtcdf: X_LO and X_UP must be of the same size.> ...
 mvtcdf ([1, 2, 3], [2, 3], ones (2), 1)
***** error<mvtcdf: correlation matrix RHO must be positive semi-definite.> ...
 mvtcdf ([2, 3], ones (2), [1, 2, 3])
***** error<mvtcdf: DF must be a scalar or a vector with the same samples as in> ...
 mvtcdf ([2, 3], [1, 0.5; 0.5, 1], [1, 2, 3])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wblcdf.m
***** demo
 ## Plot various CDFs from the Weibull distribution
 x = 0:0.001:2.5;
 p1 = wblcdf (x, 1, 0.5);
 p2 = wblcdf (x, 1, 1);
 p3 = wblcdf (x, 1, 1.5);
 p4 = wblcdf (x, 1, 5);
 plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
 grid on
 legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
          "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
 title ("Weibull CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0, 1-exp(-x(2:4)), 1];
***** assert (wblcdf (x, ones (1,5), ones (1,5)), y, 1e-16)
***** assert (wblcdf (x, ones (1,5), ones (1,5), "upper"), 1 - y)
***** assert (wblcdf (x, "upper"), 1 - y)
***** assert (wblcdf (x, 1, ones (1,5)), y, 1e-16)
***** assert (wblcdf (x, ones (1,5), 1), y, 1e-16)
***** assert (wblcdf (x, [0 1 NaN Inf 1], 1), [NaN 0 NaN 0 1])
***** assert (wblcdf (x, [0 1 NaN Inf 1], 1, "upper"), 1 - [NaN 0 NaN 0 1])
***** assert (wblcdf (x, 1, [0 1 NaN Inf 1]), [NaN 0 NaN y(4:5)])
***** assert (wblcdf (x, 1, [0 1 NaN Inf 1], "upper"), 1 - [NaN 0 NaN y(4:5)])
***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1), [y(1:2) NaN y(4:5)])
***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1, "upper"), 1 - [y(1:2) NaN y(4:5)])
***** assert (wblcdf ([x, NaN], 1, 1), [y, NaN], 1e-16)
***** assert (wblcdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (wblcdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (wblcdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<wblcdf: invalid number of input arguments.> wblcdf ()
***** error<wblcdf: invalid number of input arguments.> wblcdf (1,2,3,4,5,6,7)
***** error<wblcdf: invalid argument for upper tail.> wblcdf (1, 2, 3, 4, "uper")
***** error<wblcdf: X, LAMBDA, and K must be of common size or scalars.> ...
 wblcdf (ones (3), ones (2), ones (2))
***** error<wblcdf: invalid size of covariance matrix.> wblcdf (2, 3, 4, [1, 2])
***** error<wblcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = wblcdf (1, 2, 3)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (i, 2, 2)
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, i, 2)
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, 2, i)
***** error<wblcdf: bad covariance matrix.> ...
 [p, plo, pup] =wblcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/laplacernd.m
***** assert (size (laplacernd (1, 1)), [1 1])
***** assert (size (laplacernd (1, ones (2,1))), [2, 1])
***** assert (size (laplacernd (1, ones (2,2))), [2, 2])
***** assert (size (laplacernd (ones (2,1), 1)), [2, 1])
***** assert (size (laplacernd (ones (2,2), 1)), [2, 2])
***** assert (size (laplacernd (1, 1, 3)), [3, 3])
***** assert (size (laplacernd (1, 1, [4, 1])), [4, 1])
***** assert (size (laplacernd (1, 1, 4, 1)), [4, 1])
***** assert (size (laplacernd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (laplacernd (1, 1, 0, 1)), [0, 1])
***** assert (size (laplacernd (1, 1, 1, 0)), [1, 0])
***** assert (size (laplacernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (laplacernd (1, 1)), "double")
***** assert (class (laplacernd (1, single (1))), "single")
***** assert (class (laplacernd (1, single ([1, 1]))), "single")
***** assert (class (laplacernd (single (1), 1)), "single")
***** assert (class (laplacernd (single ([1, 1]), 1)), "single")
***** error<laplacernd: function called with too few input arguments.> laplacernd ()
***** error<laplacernd: function called with too few input arguments.> laplacernd (1)
***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
 laplacernd (ones (3), ones (2))
***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
 laplacernd (ones (2), ones (3))
***** error<laplacernd: MU and BETA must not be complex.> laplacernd (i, 2, 3)
***** error<laplacernd: MU and BETA must not be complex.> laplacernd (1, i, 3)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, -1)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, 1.2)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, ones (2))
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, [2 -1 2])
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, [2 0 2.5])
***** error<laplacernd: dimensions must be non-negative integers.> ...
 laplacernd (1, 2, 2, -1, 5)
***** error<laplacernd: dimensions must be non-negative integers.> ...
 laplacernd (1, 2, 2, 1.5, 5)
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), 3)
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), [3, 2])
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nctcdf.m
***** demo
 ## Plot various CDFs from the noncentral Τ distribution
 x = -5:0.01:5;
 p1 = nctcdf (x, 1, 0);
 p2 = nctcdf (x, 4, 0);
 p3 = nctcdf (x, 1, 2);
 p4 = nctcdf (x, 4, 2);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
 grid on
 xlim ([-5, 5])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "southeast")
 title ("Noncentral Τ CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral T CDF with MU = 1 to the T CDF
 ## with the same number of degrees of freedom (10).

 x = -5:0.1:5;
 p1 = nctcdf (x, 10, 1);
 p2 = tcdf (x, 10);
 plot (x, p1, "-", x, p2, "-")
 grid on
 xlim ([-5, 5])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "southeast")
 title ("Noncentral T vs T CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = nctcdf (x, 10, 1);
 assert (p(1), 0.003302485766631558, 1e-14);
 assert (p(2), 0.004084668193532631, 1e-14);
 assert (p(3), 0.005052800319478737, 1e-14);
 assert (p(41), 0.8076115625303751, 1e-14);
***** test
 p = nctcdf (12, 10, 3);
 assert (p, 0.9997719343243797, 1e-14);
***** test
 p = nctcdf (2, 3, 2);
 assert (p, 0.4430757822176028, 1e-14);
***** test
 p = nctcdf (2, 3, 2, "upper");
 assert (p, 0.5569242177823971, 1e-14);
***** test
 p = nctcdf ([3, 6], 3, 2, "upper");
 assert (p, [0.3199728259444777, 0.07064855592441913], 1e-14);
***** error<nctcdf: function called with too few input arguments.> nctcdf ()
***** error<nctcdf: function called with too few input arguments.> nctcdf (1)
***** error<nctcdf: function called with too few input arguments.> nctcdf (1, 2)
***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, "tail")
***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, 4)
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (3), ones (2), ones (2))
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (2), ones (3), ones (2))
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (2), ones (2), ones (3))
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (i, 2, 2)
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, i, 2)
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2rnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncx2rnd.m
***** assert (size (ncx2rnd (1, 1)), [1 1])
***** assert (size (ncx2rnd (1, ones (2,1))), [2, 1])
***** assert (size (ncx2rnd (1, ones (2,2))), [2, 2])
***** assert (size (ncx2rnd (ones (2,1), 1)), [2, 1])
***** assert (size (ncx2rnd (ones (2,2), 1)), [2, 2])
***** assert (size (ncx2rnd (1, 1, 3)), [3, 3])
***** assert (size (ncx2rnd (1, 1, [4, 1])), [4, 1])
***** assert (size (ncx2rnd (1, 1, 4, 1)), [4, 1])
***** assert (size (ncx2rnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ncx2rnd (1, 1, 0, 1)), [0, 1])
***** assert (size (ncx2rnd (1, 1, 1, 0)), [1, 0])
***** assert (size (ncx2rnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (ncx2rnd (1, 1)), "double")
***** assert (class (ncx2rnd (1, single (1))), "single")
***** assert (class (ncx2rnd (1, single ([1, 1]))), "single")
***** assert (class (ncx2rnd (single (1), 1)), "single")
***** assert (class (ncx2rnd (single ([1, 1]), 1)), "single")
***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd ()
***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd (1)
***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
 ncx2rnd (ones (3), ones (2))
***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
 ncx2rnd (ones (2), ones (3))
***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (i, 2)
***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (1, i)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, -1)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, 1.2)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, ones (2))
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, [2 -1 2])
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, [2 0 2.5])
***** error<ncx2rnd: dimensions must be non-negative integers.> ...
 ncx2rnd (1, 2, 2, -1, 5)
***** error<ncx2rnd: dimensions must be non-negative integers.> ...
 ncx2rnd (1, 2, 2, 1.5, 5)
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), 3)
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), [3, 2])
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ricecdf.m
***** demo
 ## Plot various CDFs from the Rician distribution
 x = 0:0.01:10;
 p1 = ricecdf (x, 0, 1);
 p2 = ricecdf (x, 0.5, 1);
 p3 = ricecdf (x, 1, 1);
 p4 = ricecdf (x, 2, 1);
 p5 = ricecdf (x, 4, 1);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 xlim ([0, 8])
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "southeast")
 title ("Rician CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot various CDFs from the Rician distribution
 x = 0:0.01:10;
 p1 = ricecdf (x, 0, 0.5);
 p2 = ricecdf (x, 0, 2);
 p3 = ricecdf (x, 0, 3);
 p4 = ricecdf (x, 2, 2);
 p5 = ricecdf (x, 4, 2);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 xlim ([0, 8])
 legend ({"ν = 0, σ = 0.5", "ν = 0, σ = 2", "ν = 0, σ = 3", ...
          "ν = 2, σ = 2", "ν = 4, σ = 2"}, "location", "southeast")
 title ("Rician CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 s = 1:6;
 p = ricecdf (x, s, 1);
 expected_p = [0.0000, 0.0179, 0.0108, 0.0034, 0.0008, 0.0001];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 p = ricecdf (x, 1, sigma);
 expected_p = [0.0000, 0.0272, 0.0512, 0.0659, 0.0754, 0.0820];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = ricecdf (x, 0, 1);
 expected_p = [0.0000, 0.1175, 0.3935, 0.6753, 0.8647, 0.9561];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = ricecdf (x, 1, 1);
 expected_p = [0.0000, 0.0735, 0.2671, 0.5120, 0.7310, 0.8791];
 assert (p, expected_p, 0.001);
***** shared x, p
 x = [-1, 0, 1, 2, Inf];
 p = [0, 0, 0.26712019620318, 0.73098793996409, 1];
***** assert (ricecdf (x, 1, 1), p, 1e-14)
***** assert (ricecdf (x, 1, 1, "upper"), 1 - p, 1e-14)
***** error<ricecdf: function called with too few input arguments.> ricecdf ()
***** error<ricecdf: function called with too few input arguments.> ricecdf (1)
***** error<ricecdf: function called with too few input arguments.> ricecdf (1, 2)
***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, "uper")
***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, 4)
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (3), ones (2), ones (2))
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (2), ones (3), ones (2))
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (2), ones (2), ones (3))
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (i, 2, 3)
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, i, 3)
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/poissinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/poissinv.m
***** demo
 ## Plot various iCDFs from the Poisson distribution
 p = 0.001:0.001:0.999;
 x1 = poissinv (p, 13);
 x2 = poissinv (p, 4);
 x3 = poissinv (p, 10);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 20])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northwest")
 title ("Poisson iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of occurences)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (poissinv (p, ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (poissinv (p, 1), [NaN 0 1 Inf NaN])
***** assert (poissinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
***** assert (poissinv ([p(1:2) NaN p(4:5)], 1), [NaN 0 NaN Inf NaN])
***** assert (poissinv ([p, NaN], 1), [NaN 0 1 Inf NaN NaN])
***** assert (poissinv (single ([p, NaN]), 1), single ([NaN 0 1 Inf NaN NaN]))
***** assert (poissinv ([p, NaN], single (1)), single ([NaN 0 1 Inf NaN NaN]))
***** error<poissinv: function called with too few input arguments.> poissinv ()
***** error<poissinv: function called with too few input arguments.> poissinv (1)
***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
 poissinv (ones (3), ones (2))
***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
 poissinv (ones (2), ones (3))
***** error<poissinv: P and LAMBDA must not be complex.> poissinv (i, 2)
***** error<poissinv: P and LAMBDA must not be complex.> poissinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchycdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/cauchycdf.m
***** demo
 ## Plot various CDFs from the Cauchy distribution
 x = -5:0.01:5;
 p1 = cauchycdf (x, 0, 0.5);
 p2 = cauchycdf (x, 0, 1);
 p3 = cauchycdf (x, 0, 2);
 p4 = cauchycdf (x, -2, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-5, 5])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "southeast")
 title ("Cauchy CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = 1/pi * atan ((x-1) / 2) + 1/2;
***** assert (cauchycdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (cauchycdf (x, 1, 2*ones (1,5)), y)
***** assert (cauchycdf (x, ones (1,5), 2), y)
***** assert (cauchycdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
***** assert (cauchycdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
***** assert (cauchycdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
***** assert (cauchycdf ([x, NaN], 1, 2), [y, NaN])
***** assert (cauchycdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
***** assert (cauchycdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
***** assert (cauchycdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
***** error<cauchycdf: function called with too few input arguments.> cauchycdf ()
***** error<cauchycdf: function called with too few input arguments.> cauchycdf (1)
***** error<cauchycdf: function called with too few input arguments.> ...
 cauchycdf (1, 2)
***** error<cauchycdf: function called with too many inputs> ...
 cauchycdf (1, 2, 3, 4, 5)
***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, "tail")
***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, 4)
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (3), ones (2), ones (2))
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (2), ones (3), ones (2))
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (2), ones (2), ones (3))
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (i, 2, 2)
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, i, 2)
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, 2, i)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/evinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/evinv.m
***** demo
 ## Plot various iCDFs from the extreme value distribution
 p = 0.001:0.001:0.999;
 x1 = evinv (p, 0.5, 2);
 x2 = evinv (p, 1.0, 2);
 x3 = evinv (p, 1.5, 3);
 x4 = evinv (p, 3.0, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-10, 10])
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northwest")
 title ("Extreme value iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [0, 0.05, 0.5 0.95];
 x = [-Inf, -2.9702, -0.3665, 1.0972];
***** assert (evinv (p), x, 1e-4)
***** assert (evinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
***** assert (evinv (p, 0, ones (1,4)), x, 1e-4)
***** assert (evinv (p, zeros (1,4), 1), x, 1e-4)
***** assert (evinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
***** assert (evinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
***** assert (evinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
***** assert (evinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
***** assert (evinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
***** assert (evinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
***** assert (evinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
***** error<evinv: invalid number of input arguments.> evinv ()
***** error evinv (1,2,3,4,5,6)
***** error<evinv: P, MU, and SIGMA must be of common size or scalars.> ...
 evinv (ones (3), ones (2), ones (2))
***** error<evinv: invalid size of covariance matrix.> ...
 [p, plo, pup] = evinv (2, 3, 4, [1, 2])
***** error<evinv: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = evinv (1, 2, 3)
***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
 evinv (1, 2, 3, [1, 0; 0, 1], 0)
***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
 evinv (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (i, 2, 2)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, i, 2)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, 2, i)
***** error<evinv: bad covariance matrix.> ...
 [p, plo, pup] = evinv (1, 2, 3, [-1, -10; -Inf, -Inf], 0.04)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wblpdf.m
***** demo
 ## Plot various PDFs from the Weibul distribution
 x = 0:0.001:2.5;
 y1 = wblpdf (x, 1, 0.5);
 y2 = wblpdf (x, 1, 1);
 y3 = wblpdf (x, 1, 1.5);
 y4 = wblpdf (x, 1, 5);
 plot (x, y1, "-b", x, y2, "-r", x, y3, "-m", x, y4, "-g")
 grid on
 ylim ([0, 2.5])
 legend ({"λ = 5, k = 0.5", "λ = 9, k = 1", ...
          "λ = 6, k = 1.5", "λ = 2, k = 5"}, "location", "northeast")
 title ("Weibul PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x,y
 x = [-1 0 0.5 1 Inf];
 y = [0, exp(-x(2:4)), NaN];
***** assert (wblpdf (x, ones (1,5), ones (1,5)), y)
***** assert (wblpdf (x, 1, ones (1,5)), y)
***** assert (wblpdf (x, ones (1,5), 1), y)
***** assert (wblpdf (x, [0 NaN Inf 1 1], 1), [NaN NaN NaN y(4:5)])
***** assert (wblpdf (x, 1, [0 NaN Inf 1 1]), [NaN NaN NaN y(4:5)])
***** assert (wblpdf ([x, NaN], 1, 1), [y, NaN])
***** assert (wblpdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (wblpdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (wblpdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error wblpdf ()
***** error wblpdf (1,2,3,4)
***** error wblpdf (ones (3), ones (2), ones (2))
***** error wblpdf (ones (2), ones (3), ones (2))
***** error wblpdf (ones (2), ones (2), ones (3))
***** error wblpdf (i, 2, 2)
***** error wblpdf (2, i, 2)
***** error wblpdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/finv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/finv.m
***** demo
 ## Plot various iCDFs from the F distribution
 p = 0.001:0.001:0.999;
 x1 = finv (p, 1, 1);
 x2 = finv (p, 2, 1);
 x3 = finv (p, 5, 2);
 x4 = finv (p, 10, 1);
 x5 = finv (p, 100, 100);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 4])
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "northwest")
 title ("F iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (finv (p, 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (finv (p, 2, 2*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (finv (p, 2*ones (1,5), 2), [NaN 0 1 Inf NaN])
***** assert (finv (p, [2 -Inf NaN Inf 2], 2), [NaN NaN NaN NaN NaN])
***** assert (finv (p, 2, [2 -Inf NaN Inf 2]), [NaN NaN NaN NaN NaN])
***** assert (finv ([p(1:2) NaN p(4:5)], 2, 2), [NaN 0 NaN Inf NaN])
***** assert (finv (0.025, 10, 1e6), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e7), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e10), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e255), 0.3247, 1e-4)
***** assert (finv (0.025, 10, Inf), 0.3247, 1e-4)
***** assert (finv ([p, NaN], 2, 2), [NaN 0 1 Inf NaN NaN])
***** assert (finv (single ([p, NaN]), 2, 2), single ([NaN 0 1 Inf NaN NaN]))
***** assert (finv ([p, NaN], single (2), 2), single ([NaN 0 1 Inf NaN NaN]))
***** assert (finv ([p, NaN], 2, single (2)), single ([NaN 0 1 Inf NaN NaN]))
***** error<finv: function called with too few input arguments.> finv ()
***** error<finv: function called with too few input arguments.> finv (1)
***** error<finv: function called with too few input arguments.> finv (1,2)
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (3), ones (2), ones (2))
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (2), ones (3), ones (2))
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (2), ones (2), ones (3))
***** error<finv: P, DF1, and DF2 must not be complex.> finv (i, 2, 2)
***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, i, 2)
***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, 2, i)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/lognrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/lognrnd.m
***** assert (size (lognrnd (1, 1)), [1 1])
***** assert (size (lognrnd (1, ones (2,1))), [2, 1])
***** assert (size (lognrnd (1, ones (2,2))), [2, 2])
***** assert (size (lognrnd (ones (2,1), 1)), [2, 1])
***** assert (size (lognrnd (ones (2,2), 1)), [2, 2])
***** assert (size (lognrnd (1, 1, 3)), [3, 3])
***** assert (size (lognrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (lognrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (lognrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (lognrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (lognrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (lognrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (lognrnd (1, 1)), "double")
***** assert (class (lognrnd (1, single (1))), "single")
***** assert (class (lognrnd (1, single ([1, 1]))), "single")
***** assert (class (lognrnd (single (1), 1)), "single")
***** assert (class (lognrnd (single ([1, 1]), 1)), "single")
***** error<lognrnd: function called with too few input arguments.> lognrnd ()
***** error<lognrnd: function called with too few input arguments.> lognrnd (1)
***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
 lognrnd (ones (3), ones (2))
***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
 lognrnd (ones (2), ones (3))
***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (i, 2, 3)
***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (1, i, 3)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, -1)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, 1.2)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, ones (2))
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, [2 -1 2])
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, [2 0 2.5])
***** error<lognrnd: dimensions must be non-negative integers.> ...
 lognrnd (1, 2, 2, -1, 5)
***** error<lognrnd: dimensions must be non-negative integers.> ...
 lognrnd (1, 2, 2, 1.5, 5)
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), 3)
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), [3, 2])
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/mnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mnrnd.m
***** test
 n = 10;
 pk = [0.2, 0.5, 0.3];
 r = mnrnd (n, pk);
 assert (size (r), size (pk));
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (sum (r) == n);
***** test
 n = 10 * ones (3, 1);
 pk = [0.2, 0.5, 0.3];
 r = mnrnd (n, pk);
 assert (size (r), [length(n), length(pk)]);
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (all (sum (r, 2) == n));
***** test
 n = (1:2)';
 pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
 r = mnrnd (n, pk);
 assert (size (r), size (pk));
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (all (sum (r, 2) == n));
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbininv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nbininv.m
***** demo
 ## Plot various iCDFs from the negative binomial distribution
 p = 0.001:0.001:0.999;
 x1 = nbininv (p, 2, 0.15);
 x2 = nbininv (p, 5, 0.2);
 x3 = nbininv (p, 4, 0.4);
 x4 = nbininv (p, 10, 0.3);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([0, 40])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "northwest")
 title ("Negative binomial iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of failures)")
***** shared p
 p = [-1 0 3/4 1 2];
***** assert (nbininv (p, ones (1,5), 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, 1, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, ones (1,5), 0.5), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, [1 0 NaN Inf 1], 0.5), [NaN NaN NaN NaN NaN])
***** assert (nbininv (p, [1 0 1.5 Inf 1], 0.5), [NaN NaN 2 NaN NaN])
***** assert (nbininv (p, 1, 0.5*[1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (nbininv ([p(1:2) NaN p(4:5)], 1, 0.5), [NaN 0 NaN Inf NaN])
***** assert (nbininv ([p, NaN], 1, 0.5), [NaN 0 1 Inf NaN NaN])
***** assert (nbininv (single ([p, NaN]), 1, 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (nbininv ([p, NaN], single (1), 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (nbininv ([p, NaN], 1, single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
***** shared y, tol
 y = magic (3) + 1;
 tol = 1;
***** assert (nbininv (nbincdf (1:10, 3, 0.1), 3, 0.1), 1:10, tol)
***** assert (nbininv (nbincdf (1:10, 3./(1:10), 0.1), 3./(1:10), 0.1), 1:10, tol)
***** assert (nbininv (nbincdf (y, 3./y, 1./y), 3./y, 1./y), y, tol)
***** error<nbininv: function called with too few input arguments.> nbininv ()
***** error<nbininv: function called with too few input arguments.> nbininv (1)
***** error<nbininv: function called with too few input arguments.> nbininv (1, 2)
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (3), ones (2), ones (2))
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (2), ones (3), ones (2))
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (2), ones (2), ones (3))
***** error<nbininv: P, R, and PS must not be complex.> nbininv (i, 2, 2)
***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, i, 2)
***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, 2, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/burrrnd.m
***** assert (size (burrrnd (1, 1, 1)), [1 1])
***** assert (size (burrrnd (ones (2,1), 1, 1)), [2, 1])
***** assert (size (burrrnd (ones (2,2), 1, 1)), [2, 2])
***** assert (size (burrrnd (1, ones (2,1), 1)), [2, 1])
***** assert (size (burrrnd (1, ones (2,2), 1)), [2, 2])
***** assert (size (burrrnd (1, 1, ones (2,1))), [2, 1])
***** assert (size (burrrnd (1, 1, ones (2,2))), [2, 2])
***** assert (size (burrrnd (1, 1, 1, 3)), [3, 3])
***** assert (size (burrrnd (1, 1, 1, [4 1])), [4, 1])
***** assert (size (burrrnd (1, 1, 1, 4, 1)), [4, 1])
***** assert (class (burrrnd (1,1,1)), "double")
***** assert (class (burrrnd (single (1),1,1)), "single")
***** assert (class (burrrnd (single ([1 1]),1,1)), "single")
***** assert (class (burrrnd (1,single (1),1)), "single")
***** assert (class (burrrnd (1,single ([1 1]),1)), "single")
***** assert (class (burrrnd (1,1,single (1))), "single")
***** assert (class (burrrnd (1,1,single ([1 1]))), "single")
***** error<burrrnd: function called with too few input arguments.> burrrnd ()
***** error<burrrnd: function called with too few input arguments.> burrrnd (1)
***** error<burrrnd: function called with too few input arguments.> burrrnd (1, 2)
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (3), ones (2), ones (2))
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (2), ones (3), ones (2))
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (2), ones (2), ones (3))
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (i, 2, 3)
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, i, 3)
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, 2, i)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, -1)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, 1.2)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, ones (2))
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, [2 -1 2])
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, [2 0 2.5])
***** error<burrrnd: dimensions must be non-negative integers.> ...
 burrrnd (1, 2, 3, 2, -1, 5)
***** error<burrrnd: dimensions must be non-negative integers.> ...
 burrrnd (1, 2, 3, 2, 1.5, 5)
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, 3)
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, [3, 2])
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, 3, 2)
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_fun/wishrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wishrnd.m
***** assert(size (wishrnd (1,2)), [1, 1]);
***** assert(size (wishrnd (1,2,[])), [1, 1]);
***** assert(size (wishrnd (1,2,1)), [1, 1]);
***** assert(size (wishrnd ([],2,1)), [1, 1]);
***** assert(size (wishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
***** assert(size (wishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
***** error wishrnd ()
***** error wishrnd (1)
***** error wishrnd ([1; 1], 2)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbinpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nbinpdf.m
***** demo
 ## Plot various PDFs from the negative binomial distribution
 x = 0:40;
 y1 = nbinpdf (x, 2, 0.15);
 y2 = nbinpdf (x, 5, 0.2);
 y3 = nbinpdf (x, 4, 0.4);
 y4 = nbinpdf (x, 10, 0.3);
 plot (x, y1, "*r", x, y2, "*g", x, y3, "*k", x, y4, "*m")
 grid on
 xlim ([0, 40])
 ylim ([0, 0.12])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "northeast")
 title ("Negative binomial PDF")
 xlabel ("values in x (number of failures)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0 1/2 1/4 1/8 NaN];
***** assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y)
***** assert (nbinpdf (x, 1, 0.5*ones (1,5)), y)
***** assert (nbinpdf (x, ones (1,5), 0.5), y)
***** assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps)
***** assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
***** assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN])
***** assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
***** assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
***** assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
***** error<nbinpdf: function called with too few input arguments.> nbinpdf ()
***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1)
***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1, 2)
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (3), ones (2), ones (2))
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (2), ones (3), ones (2))
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (2), ones (2), ones (3))
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (i, 2, 2)
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, i, 2)
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2rnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/chi2rnd.m
***** assert (size (chi2rnd (2)), [1, 1])
***** assert (size (chi2rnd (ones (2,1))), [2, 1])
***** assert (size (chi2rnd (ones (2,2))), [2, 2])
***** assert (size (chi2rnd (1, 3)), [3, 3])
***** assert (size (chi2rnd (1, [4 1])), [4, 1])
***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
***** assert (size (chi2rnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (chi2rnd (1, 0, 1)), [0, 1])
***** assert (size (chi2rnd (1, 1, 0)), [1, 0])
***** assert (size (chi2rnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (chi2rnd (2)), "double")
***** assert (class (chi2rnd (single (2))), "single")
***** assert (class (chi2rnd (single ([2 2]))), "single")
***** error<chi2rnd: function called with too few input arguments.> chi2rnd ()
***** error<chi2rnd: DF must not be complex.> chi2rnd (i)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, -1)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, 1.2)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, ones (2))
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, [2 -1 2])
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, [2 0 2.5])
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (ones (2), ones (2))
***** error<chi2rnd: dimensions must be non-negative integers.> ...
 chi2rnd (1, 2, -1, 5)
***** error<chi2rnd: dimensions must be non-negative integers.> ...
 chi2rnd (1, 2, 1.5, 5)
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 3)
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), [3, 2])
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 2, 3)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/invgrnd.m
***** assert (size (invgrnd (1, 1, 1)), [1, 1])
***** assert (size (invgrnd (1, 1, 2)), [2, 2])
***** assert (size (invgrnd (1, 1, [2, 1])), [2, 1])
***** assert (size (invgrnd (1, zeros (2, 2))), [2, 2])
***** assert (size (invgrnd (1, ones (2, 1))), [2, 1])
***** assert (size (invgrnd (1, ones (2, 2))), [2, 2])
***** assert (size (invgrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (invgrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (invgrnd (1, 1, 3)), [3, 3])
***** assert (size (invgrnd (1, 1, [4 1])), [4, 1])
***** assert (size (invgrnd (1, 1, 4, 1)), [4, 1])
***** test
 r =  invgrnd (1, [1, 0, -1]);
 assert (r([2:3]), [NaN, NaN])
***** assert (class (invgrnd (1, 0)), "double")
***** assert (class (invgrnd (1, single (0))), "single")
***** assert (class (invgrnd (1, single ([0 0]))), "single")
***** assert (class (invgrnd (1, single (1))), "single")
***** assert (class (invgrnd (1, single ([1 1]))), "single")
***** assert (class (invgrnd (single (1), 1)), "single")
***** assert (class (invgrnd (single ([1 1]), 1)), "single")
***** error<invgrnd: function called with too few input arguments.> invgrnd ()
***** error<invgrnd: function called with too few input arguments.> invgrnd (1)
***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
 invgrnd (ones (3), ones (2))
***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
 invgrnd (ones (2), ones (3))
***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (i, 2, 3)
***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (1, i, 3)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, -1)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, 1.2)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, ones (2))
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, [2 -1 2])
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, [2 0 2.5])
***** error<invgrnd: dimensions must be non-negative integers.> ...
 invgrnd (1, 2, 2, -1, 5)
***** error<invgrnd: dimensions must be non-negative integers.> ...
 invgrnd (1, 2, 2, 1.5, 5)
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), 3)
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), [3, 2])
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/poisscdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/poisscdf.m
***** demo
 ## Plot various CDFs from the Poisson distribution
 x = 0:20;
 p1 = poisscdf (x, 1);
 p2 = poisscdf (x, 4);
 p3 = poisscdf (x, 10);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 ylim ([0, 1])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "southeast")
 title ("Poisson CDF")
 xlabel ("values in x (number of occurences)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0, gammainc(1, (x(2:4) +1), "upper"), 1];
***** assert (poisscdf (x, ones (1,5)), y)
***** assert (poisscdf (x, 1), y)
***** assert (poisscdf (x, [1 0 NaN 1 1]), [y(1) 1 NaN y(4:5)])
***** assert (poisscdf ([x(1:2) NaN Inf x(5)], 1), [y(1:2) NaN 1 y(5)])
***** assert (poisscdf ([x, NaN], 1), [y, NaN])
***** assert (poisscdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (poisscdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<poisscdf: function called with too few input arguments.> poisscdf ()
***** error<poisscdf: function called with too few input arguments.> poisscdf (1)
***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, 3)
***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, "tail")
***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
 poisscdf (ones (3), ones (2))
***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
 poisscdf (ones (2), ones (3))
***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (i, 2)
***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/plpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/plpdf.m
***** demo
 ## Plot various PDFs from the Piecewise linear distribution
 data = 0:0.01:10;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 y1 = plpdf (data, x1, Fx1);
 y2 = plpdf (data, x2, Fx2);
 plot (data, y1, "-b", data, y2, "g")
 grid on
 ylim ([0, 0.6])
 xlim ([0, 10])
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "northeast")
 title ("Piecewise linear CDF")
 xlabel ("values in data")
 ylabel ("density")
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (plpdf (0.5, x, Fx), 0.2, eps);
***** assert (plpdf (1.5, x, Fx), 0.15, eps);
***** assert (plpdf (3.5, x, Fx), 0.1, eps);
***** assert (plpdf (5, x, Fx), 0.1/3, eps);
***** assert (plpdf (8, x, Fx), 0.1, eps);
***** error<plpdf: function called with too few input arguments.> plpdf ()
***** error<plpdf: function called with too few input arguments.> plpdf (1)
***** error<plpdf: function called with too few input arguments.> plpdf (1, 2)
***** error<plpdf: X and FX must be vectors of equal size.> ...
 plpdf (1, [0, 1, 2], [0, 1])
***** error<plpdf: X and FX must be at least two-elements long.> ...
 plpdf (1, [0], [1])
***** error<plpdf: FX must be bounded in the range> ...
 plpdf (1, [0, 1, 2], [0, 1, 1.5])
***** error<plpdf: FX must be bounded in the range> ...
 plpdf (1, [0, 1, 2], [0, i, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (i, [0, 1, 2], [0, 0.5, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (1, [0, i, 2], [0, 0.5, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (1, [0, 1, 2], [0, 0.5i, 1])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/gpcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gpcdf.m
***** demo
 ## Plot various CDFs from the generalized Pareto distribution
 x = 0:0.001:5;
 p1 = gpcdf (x, 1, 1, 0);
 p2 = gpcdf (x, 5, 1, 0);
 p3 = gpcdf (x, 20, 1, 0);
 p4 = gpcdf (x, 1, 2, 0);
 p5 = gpcdf (x, 5, 2, 0);
 p6 = gpcdf (x, 20, 2, 0);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 xlim ([0, 5])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "northwest")
 title ("Generalized Pareto CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y1, y1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y1 = [0, 0, 0, 0.3934693402873666, 0.6321205588285577, 1];
 y1u = [1, 1, 1, 0.6065306597126334, 0.3678794411714423, 0];
 y2 = [0, 0, 0, 1/3, 1/2, 1];
 y2u = [1, 1, 1, 2/3, 1/2, 0];
 y3 = [0, 0, 0, 1/2, 1, 1];
 y3u = [1, 1, 1, 1/2, 0, 0];
***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
***** assert (gpcdf (x, 0, 1, zeros (1,6)), y1, eps)
***** assert (gpcdf (x, 0, ones (1,6), 0), y1, eps)
***** assert (gpcdf (x, zeros (1,6), 1, 0), y1, eps)
***** assert (gpcdf (x, 0, 1, 0), y1, eps)
***** assert (gpcdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6), "upper"), y1u, eps)
***** assert (gpcdf (x, 0, 1, zeros (1,6), "upper"), y1u, eps)
***** assert (gpcdf (x, 0, ones (1,6), 0, "upper"), y1u, eps)
***** assert (gpcdf (x, zeros (1,6), 1, 0, "upper"), y1u, eps)
***** assert (gpcdf (x, 0, 1, 0, "upper"), y1u, eps)
***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
***** assert (gpcdf (x, 1, 1, zeros (1,6)), y2, eps)
***** assert (gpcdf (x, 1, ones (1,6), 0), y2, eps)
***** assert (gpcdf (x, ones (1,6), 1, 0), y2, eps)
***** assert (gpcdf (x, 1, 1, 0), y2, eps)
***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6), "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, zeros (1,6), "upper"), y2u, eps)
***** assert (gpcdf (x, 1, ones (1,6), 0, "upper"), y2u, eps)
***** assert (gpcdf (x, ones (1,6), 1, 0, "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, 0, "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
***** assert (gpcdf (x, -1, 1, zeros (1,6)), y3, eps)
***** assert (gpcdf (x, -1, ones (1,6), 0), y3, eps)
***** assert (gpcdf (x, -ones (1,6), 1, 0), y3, eps)
***** assert (gpcdf (x, -1, 1, 0), y3, eps)
***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6), "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, zeros (1,6), "upper"), y3u, eps)
***** assert (gpcdf (x, -1, ones (1,6), 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -ones (1,6), 1, 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
                         [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]), eps("single"))
***** error<gpcdf: function called with too few input arguments.> gpcdf ()
***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, "tail")
***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, 5)
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
76 tests, 76 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bisacdf.m
***** demo
 ## Plot various CDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:4;
 p1 = bisacdf (x, 1, 0.5);
 p2 = bisacdf (x, 1, 1);
 p3 = bisacdf (x, 1, 2);
 p4 = bisacdf (x, 1, 5);
 p5 = bisacdf (x, 1, 10);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "southeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot various CDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:6;
 p1 = bisacdf (x, 1, 0.3);
 p2 = bisacdf (x, 2, 0.3);
 p3 = bisacdf (x, 1, 0.5);
 p4 = bisacdf (x, 3, 0.5);
 p5 = bisacdf (x, 5, 0.5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "southeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 1/2, 0.76024993890652337, 1];
***** assert (bisacdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (bisacdf (x, 1, 1), y, eps)
***** assert (bisacdf (x, 1, ones (1,5)), y, eps)
***** assert (bisacdf (x, ones (1,5), 1), y, eps)
***** assert (bisacdf (x, 1, 1), y, eps)
***** assert (bisacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisacdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (bisacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisacdf: function called with too few input arguments.> bisacdf ()
***** error<bisacdf: function called with too few input arguments.> bisacdf (1)
***** error<bisacdf: function called with too few input arguments.> bisacdf (1, 2)
***** error<bisacdf: function called with too many inputs> ...
 bisacdf (1, 2, 3, 4, 5)
***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, "tail")
***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, 4)
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (3), ones (2), ones(2))
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (2), ones (3), ones(2))
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (2), ones (2), ones(3))
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (i, 4, 3)
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, i, 3)
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, 4, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/normrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/normrnd.m
***** assert (size (normrnd (1, 1)), [1 1])
***** assert (size (normrnd (1, ones (2,1))), [2, 1])
***** assert (size (normrnd (1, ones (2,2))), [2, 2])
***** assert (size (normrnd (ones (2,1), 1)), [2, 1])
***** assert (size (normrnd (ones (2,2), 1)), [2, 2])
***** assert (size (normrnd (1, 1, 3)), [3, 3])
***** assert (size (normrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (normrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (normrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (normrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (normrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (normrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (normrnd (1, 1)), "double")
***** assert (class (normrnd (1, single (1))), "single")
***** assert (class (normrnd (1, single ([1, 1]))), "single")
***** assert (class (normrnd (single (1), 1)), "single")
***** assert (class (normrnd (single ([1, 1]), 1)), "single")
***** error<normrnd: function called with too few input arguments.> normrnd ()
***** error<normrnd: function called with too few input arguments.> normrnd (1)
***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
 normrnd (ones (3), ones (2))
***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
 normrnd (ones (2), ones (3))
***** error<normrnd: MU and SIGMA must not be complex.> normrnd (i, 2, 3)
***** error<normrnd: MU and SIGMA must not be complex.> normrnd (1, i, 3)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, -1)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, 1.2)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, ones (2))
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, [2 -1 2])
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, [2 0 2.5])
***** error<normrnd: dimensions must be non-negative integers.> ...
 normrnd (1, 2, 2, -1, 5)
***** error<normrnd: dimensions must be non-negative integers.> ...
 normrnd (1, 2, 2, 1.5, 5)
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), 3)
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), [3, 2])
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hygecdf.m
***** demo
 ## Plot various CDFs from the hypergeometric distribution
 x = 0:60;
 p1 = hygecdf (x, 500, 50, 100);
 p2 = hygecdf (x, 500, 60, 200);
 p3 = hygecdf (x, 500, 70, 300);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 xlim ([0, 60])
 legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
          "m = 500, k = 70, n = 300"}, "location", "southeast")
 title ("Hypergeometric CDF")
 xlabel ("values in x (number of successes)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/6 5/6 1 1];
***** assert (hygecdf (x, 4*ones (1,5), 2, 2), y, 5*eps)
***** assert (hygecdf (x, 4, 2*ones (1,5), 2), y, 5*eps)
***** assert (hygecdf (x, 4, 2, 2*ones (1,5)), y, 5*eps)
***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2, "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2, "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN])
***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1], "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN])
***** assert (hygecdf ([x(1:2) NaN x(4:5)], 4, 2, 2), [y(1:2) NaN y(4:5)], 5*eps)
***** test
 p = hygecdf (x, 10, [1 2 3 4 5], 2, "upper");
 assert (p, [1, 34/90, 2/30, 0, 0], 10*eps);
***** test
 p = hygecdf (2*x, 10, [1 2 3 4 5], 2, "upper");
 assert (p, [1, 34/90, 0, 0, 0], 10*eps);
***** assert (hygecdf ([x, NaN], 4, 2, 2), [y, NaN], 5*eps)
***** assert (hygecdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), ...
 eps ("single"))
***** error<hygecdf: function called with too few input arguments.> hygecdf ()
***** error<hygecdf: function called with too few input arguments.> hygecdf (1)
***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2)
***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2,3)
***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,5)
***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,"uper")
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (ones (2), ones (3), 1, 1)
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (1, ones (2), ones (3), 1)
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (1, 1, ones (2), ones (3))
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (i, 2, 2, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, i, 2, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, i, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, 2, i)
32 tests, 32 passed, 0 known failure, 0 skipped
[inst/dist_fun/logninv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logninv.m
***** demo
 ## Plot various iCDFs from the log-normal distribution
 p = 0.001:0.001:0.999;
 x1 = logninv (p, 0, 1);
 x2 = logninv (p, 0, 0.5);
 x3 = logninv (p, 0, 0.25);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 3])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
         "location", "northwest")
 title ("Log-normal iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (logninv (p, ones (1,5), ones (1,5)), [NaN 0 e Inf NaN])
***** assert (logninv (p, 1, ones (1,5)), [NaN 0 e Inf NaN])
***** assert (logninv (p, ones (1,5), 1), [NaN 0 e Inf NaN])
***** assert (logninv (p, [1 1 NaN 0 1], 1), [NaN 0 NaN Inf NaN])
***** assert (logninv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (logninv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
***** assert (logninv ([p, NaN], 1, 1), [NaN 0 e Inf NaN NaN])
***** assert (logninv (single ([p, NaN]), 1, 1), single ([NaN 0 e Inf NaN NaN]))
***** assert (logninv ([p, NaN], single (1), 1), single ([NaN 0 e Inf NaN NaN]))
***** assert (logninv ([p, NaN], 1, single (1)), single ([NaN 0 e Inf NaN NaN]))
***** error logninv ()
***** error logninv (1,2,3,4)
***** error logninv (ones (3), ones (2), ones (2))
***** error logninv (ones (2), ones (3), ones (2))
***** error logninv (ones (2), ones (2), ones (3))
***** error logninv (i, 2, 2)
***** error logninv (2, i, 2)
***** error logninv (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtcdfqmc.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvtcdfqmc.m
***** error mvtcdfqmc (1, 2, 3);
***** error mvtcdfqmc (1, 2, 3, 4, 5, 6, 7, 8);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/logicdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logicdf.m
***** demo
 ## Plot various CDFs from the logistic distribution
 x = -5:0.01:20;
 p1 = logicdf (x, 5, 2);
 p2 = logicdf (x, 9, 3);
 p3 = logicdf (x, 9, 4);
 p4 = logicdf (x, 6, 2);
 p5 = logicdf (x, 2, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
 title ("Logistic CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf -log(3) 0 log(3) Inf];
 y = [0, 1/4, 1/2, 3/4, 1];
***** assert (logicdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (logicdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
***** assert (logicdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (logicdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (logicdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<logicdf: function called with too few input arguments.> logicdf ()
***** error<logicdf: function called with too few input arguments.> logicdf (1)
***** error<logicdf: function called with too few input arguments.> ...
 logicdf (1, 2)
***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, "tail")
***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, 4)
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (1, ones (2), ones (3))
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (ones (2), 1, ones (3))
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (ones (2), ones (3), 1)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (i, 2, 3)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, i, 3)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvnpdf.m
***** demo
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = mvnpdf (x, mu, sigma);
 surf (X1, X2, reshape (p, 25, 25));
***** error<mvnpdf: too few input arguments.> y = mvnpdf ();
***** error<mvnpdf: too few dimensions in X.> y = mvnpdf ([]);
***** error<mvnpdf: wrong dimensions in X.> y = mvnpdf (ones (3,3,3));
***** error<mvnpdf: columns in X and MU mismatch.> ...
 y = mvnpdf (ones (10, 2), [4, 2, 3]);
***** error<mvnpdf: rows in X and MU mismatch.> ...
 y = mvnpdf (ones (10, 2), [4, 2; 3, 2]);
***** error<mvnpdf: wrong size of MU.> ...
 y = mvnpdf (ones (10, 2), ones (3, 3, 3));
***** shared x, mu, sigma
 x = [1, 2, 5, 4, 6];
 mu = [2, 0, -1, 1, 4];
 sigma = [2, 2, 2, 2, 2];
***** assert (mvnpdf (x), 1.579343404440977e-20, 1e-30);
***** assert (mvnpdf (x, mu), 1.899325144348102e-14, 1e-25);
***** assert (mvnpdf (x, mu, sigma), 2.449062307156273e-09, 1e-20);
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fun/lognpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/lognpdf.m
***** demo
 ## Plot various PDFs from the log-normal distribution
 x = 0:0.01:5;
 y1 = lognpdf (x, 0, 1);
 y2 = lognpdf (x, 0, 0.5);
 y3 = lognpdf (x, 0, 0.25);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
 grid on
 ylim ([0, 2])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
          "location", "northeast")
 title ("Log-normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 e Inf];
 y = [0, 0, 1/(e*sqrt(2*pi)) * exp(-1/2), 0];
***** assert (lognpdf (x, zeros (1,4), ones (1,4)), y, eps)
***** assert (lognpdf (x, 0, ones (1,4)), y, eps)
***** assert (lognpdf (x, zeros (1,4), 1), y, eps)
***** assert (lognpdf (x, [0 1 NaN 0], 1), [0 0 NaN y(4)], eps)
***** assert (lognpdf (x, 0, [0 NaN Inf 1]), [NaN NaN NaN y(4)], eps)
***** assert (lognpdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (lognpdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (lognpdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (lognpdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error lognpdf ()
***** error lognpdf (1,2,3,4)
***** error lognpdf (ones (3), ones (2), ones (2))
***** error lognpdf (ones (2), ones (3), ones (2))
***** error lognpdf (ones (2), ones (2), ones (3))
***** error lognpdf (i, 2, 2)
***** error lognpdf (2, i, 2)
***** error lognpdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/gamcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gamcdf.m
***** demo
 ## Plot various CDFs from the Gamma distribution
 x = 0:0.01:20;
 p1 = gamcdf (x, 1, 2);
 p2 = gamcdf (x, 2, 2);
 p3 = gamcdf (x, 3, 2);
 p4 = gamcdf (x, 5, 1);
 p5 = gamcdf (x, 9, 0.5);
 p6 = gamcdf (x, 7.5, 1);
 p7 = gamcdf (x, 0.5, 1);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
       x, p5, "-k", x, p6, "-b", x, p7, "-c")
 grid on
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "southeast")
 title ("Gamma CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, u
 x = [-1, 0, 0.5, 1, 2, Inf];
 y = [0, gammainc(x(2:end), 1)];
 u = [0, NaN, NaN, 1, 0.1353352832366127, 0];
***** assert (gamcdf (x, ones (1,6), ones (1,6)), y, eps)
***** assert (gamcdf (x, ones (1,6), ones (1,6), []), y, eps)
***** assert (gamcdf (x, 1, ones (1,6)), y, eps)
***** assert (gamcdf (x, ones (1,6), 1), y, eps)
***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1), [1, NaN, NaN, 0, y(5:6)], eps)
***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1, "upper"), u, eps)
***** assert (gamcdf (x, 1, [0, -Inf, NaN, Inf, 1, 1]), [NaN, NaN, NaN, 0, y(5:6)], eps)
***** assert (gamcdf ([x(1:2), NaN, x(4:6)], 1, 1), [y(1:2), NaN, y(4:6)], eps)
***** assert (gamcdf ([x, NaN], 1, 1), [y, NaN])
***** assert (gamcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (gamcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (gamcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<gamcdf: invalid number of input arguments.> gamcdf ()
***** error<gamcdf: invalid number of input arguments.> gamcdf (1)
***** error<gamcdf: invalid number of input arguments.> gamcdf (1, 2, 3, 4, 5, 6, 7)
***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, "uper")
***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, 4, 5, "uper")
***** error<gamcdf: invalid size of covariance matrix.> gamcdf (2, 3, 4, [1, 2])
***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gamcdf (1, 2, 3)
***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gamcdf (1, 2, 3, "upper")
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (3), ones (2), ones (2))
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (2), ones (3), ones (2))
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (2), ones (2), ones (3))
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (i, 2, 2)
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, i, 2)
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, 2, i)
***** error<gamcdf: bad covariance matrix.> ...
 [p, plo, pup] = gamcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
30 tests, 30 passed, 0 known failure, 0 skipped
[inst/dist_fun/gppdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gppdf.m
***** demo
 ## Plot various PDFs from the generalized Pareto distribution
 x = 0:0.001:5;
 y1 = gppdf (x, 1, 1, 0);
 y2 = gppdf (x, 5, 1, 0);
 y3 = gppdf (x, 20, 1, 0);
 y4 = gppdf (x, 1, 2, 0);
 y5 = gppdf (x, 5, 2, 0);
 y6 = gppdf (x, 20, 2, 0);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 xlim ([0, 5])
 ylim ([0, 1])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "northeast")
 title ("Generalized Pareto PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y1, y2, y3
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y1 = [0, 0, 1, 0.6065306597126334, 0.36787944117144233, 0];
 y2 = [0, 0, 1, 4/9, 1/4, 0];
 y3 = [0, 0, 1, 1, 1, 0];
***** assert (gppdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
***** assert (gppdf (x, 0, 1, zeros (1,6)), y1, eps)
***** assert (gppdf (x, 0, ones (1,6), 0), y1, eps)
***** assert (gppdf (x, zeros (1,6), 1, 0), y1, eps)
***** assert (gppdf (x, 0, 1, 0), y1, eps)
***** assert (gppdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
***** assert (gppdf (x, 1, 1, zeros (1,6)), y2, eps)
***** assert (gppdf (x, 1, ones (1,6), 0), y2, eps)
***** assert (gppdf (x, ones (1,6), 1, 0), y2, eps)
***** assert (gppdf (x, 1, 1, 0), y2, eps)
***** assert (gppdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
***** assert (gppdf (x, -1, 1, zeros (1,6)), y3, eps)
***** assert (gppdf (x, -1, ones (1,6), 0), y3, eps)
***** assert (gppdf (x, -ones (1,6), 1, 0), y3, eps)
***** assert (gppdf (x, -1, 1, 0), y3, eps)
***** assert (gppdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]))
***** assert (gppdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]))
***** assert (gppdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]))
***** error<gpcdf: function called with too few input arguments.> gpcdf ()
***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
51 tests, 51 passed, 0 known failure, 0 skipped
[inst/dist_fun/gprnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gprnd.m
***** assert (size (gprnd (0, 1, 0)), [1, 1])
***** assert (size (gprnd (0, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (0, 1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (0, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (0, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (zeros (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (zeros (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (0, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (0, 1, 0, [4 1])), [4, 1])
***** assert (size (gprnd (0, 1, 0, 4, 1)), [4, 1])
***** assert (size (gprnd (1,1,0)), [1, 1])
***** assert (size (gprnd (1, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (1, 1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (1, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (1, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (ones (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (ones (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (1, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (1, 1, 0, [4 1])), [4, 1])
***** assert (size (gprnd (1, 1, 0, 4, 1)), [4, 1])
***** assert (size (gprnd (-1, 1, 0)), [1, 1])
***** assert (size (gprnd (-1, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (1, -1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (-1, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (-1, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (-ones (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (-ones (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (-1, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (-1, 1, 0, [4, 1])), [4, 1])
***** assert (size (gprnd (-1, 1, 0, 4, 1)), [4, 1])
***** assert (class (gprnd (0, 1, 0)), "double")
***** assert (class (gprnd (0, 1, single (0))), "single")
***** assert (class (gprnd (0, 1, single ([0, 0]))), "single")
***** assert (class (gprnd (0, single (1),0)), "single")
***** assert (class (gprnd (0, single ([1, 1]),0)), "single")
***** assert (class (gprnd (single (0), 1, 0)), "single")
***** assert (class (gprnd (single ([0, 0]), 1, 0)), "single")
***** error<gprnd: function called with too few input arguments.> gprnd ()
***** error<gprnd: function called with too few input arguments.> gprnd (1)
***** error<gprnd: function called with too few input arguments.> gprnd (1, 2)
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (3), ones (2), ones (2))
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (2), ones (3), ones (2))
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (2), ones (2), ones (3))
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (i, 2, 3)
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, i, 3)
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, 2, i)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, -1)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, 1.2)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, ones (2))
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, [2 -1 2])
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, [2 0 2.5])
***** error<gprnd: dimensions must be non-negative integers.> ...
 gprnd (1, 2, 3, 2, -1, 5)
***** error<gprnd: dimensions must be non-negative integers.> ...
 gprnd (1, 2, 3, 2, 1.5, 5)
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, 3)
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, [3, 2])
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, 3, 2)
56 tests, 56 passed, 0 known failure, 0 skipped
[inst/dist_fun/wishpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wishpdf.m
***** assert(wishpdf(4, 3, 3.1), 0.07702496, 1E-7);
***** assert(wishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 0.004529741, 1E-7);
***** assert(wishpdf([6 2 5; 2 10 -5; 5 -5 25], [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.474865e-10, 1E-15);
***** error wishpdf ()
***** error wishpdf (1, 2)
***** error wishpdf (1, 2, 0)
***** error wishpdf (1, 2)
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncfrnd.m
***** assert (size (ncfrnd (1, 1, 1)), [1 1])
***** assert (size (ncfrnd (1, ones (2,1), 1)), [2, 1])
***** assert (size (ncfrnd (1, ones (2,2), 1)), [2, 2])
***** assert (size (ncfrnd (ones (2,1), 1, 1)), [2, 1])
***** assert (size (ncfrnd (ones (2,2), 1, 1)), [2, 2])
***** assert (size (ncfrnd (1, 1, 1, 3)), [3, 3])
***** assert (size (ncfrnd (1, 1, 1, [4, 1])), [4, 1])
***** assert (size (ncfrnd (1, 1, 1, 4, 1)), [4, 1])
***** assert (size (ncfrnd (1, 1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ncfrnd (1, 1, 1, 0, 1)), [0, 1])
***** assert (size (ncfrnd (1, 1, 1, 1, 0)), [1, 0])
***** assert (size (ncfrnd (1, 1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (ncfrnd (1, 1, 1)), "double")
***** assert (class (ncfrnd (1, single (1), 1)), "single")
***** assert (class (ncfrnd (1, 1, single (1))), "single")
***** assert (class (ncfrnd (1, single ([1, 1]), 1)), "single")
***** assert (class (ncfrnd (1, 1, single ([1, 1]))), "single")
***** assert (class (ncfrnd (single (1), 1, 1)), "single")
***** assert (class (ncfrnd (single ([1, 1]), 1, 1)), "single")
***** error<ncfrnd: function called with too few input arguments.> ncfrnd ()
***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1)
***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1, 2)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (3), ones (2), ones (2))
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (2), ones (3), ones (2))
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (2), ones (2), ones (3))
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (i, 2, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, i, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, 2, i)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, -1)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, 1.2)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, ones (2))
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, [2 -1 2])
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, [2 0 2.5])
***** error<ncfrnd: dimensions must be non-negative integers.> ...
 ncfrnd (1, 2, 3, 2, -1, 5)
***** error<ncfrnd: dimensions must be non-negative integers.> ...
 ncfrnd (1, 2, 3, 2, 1.5, 5)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, [3, 2])
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, 3, 2)
38 tests, 38 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlspdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tlspdf.m
***** demo
 ## Plot various PDFs from the Student's T distribution
 x = -8:0.01:8;
 y1 = tlspdf (x, 0, 1, 1);
 y2 = tlspdf (x, 0, 2, 2);
 y3 = tlspdf (x, 3, 2, 5);
 y4 = tlspdf (x, -1, 3, Inf);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
 grid on
 xlim ([-8, 8])
 ylim ([0, 0.41])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "northwest")
 title ("Location-scale Student's T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = rand (10,1);
 y = 1./(pi * (1 + x.^2));
 assert (tlspdf (x, 0, 1, 1), y, 5*eps);
 assert (tlspdf (x+5, 5, 1, 1), y, 5*eps);
 assert (tlspdf (x.*2, 0, 2, 1), y./2, 5*eps);
***** shared x, y
 x = [-Inf 0 0.5 1 Inf];
 y = 1./(pi * (1 + x.^2));
***** assert (tlspdf (x, 0, 1, ones (1,5)), y, eps)
***** assert (tlspdf (x, 0, 1, 1), y, eps)
***** assert (tlspdf (x, 0, 1, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
***** assert (tlspdf (x, 0, 1, Inf), normpdf (x))
***** assert (class (tlspdf ([x, NaN], 1, 1, 1)), "double")
***** assert (class (tlspdf (single ([x, NaN]), 1, 1, 1)), "single")
***** assert (class (tlspdf ([x, NaN], single (1), 1, 1)), "single")
***** assert (class (tlspdf ([x, NaN], 1, single (1), 1)), "single")
***** assert (class (tlspdf ([x, NaN], 1, 1, single (1))), "single")
***** error<tlspdf: function called with too few input arguments.> tlspdf ()
***** error<tlspdf: function called with too few input arguments.> tlspdf (1)
***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2)
***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2, 3)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (3), ones (2), 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (2), 1, ones (3), 1)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (2), 1, 1, ones (3))
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (i, 2, 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, i, 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, i, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, 1, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/burrinv.m
***** demo
 ## Plot various iCDFs from the Burr type XII distribution
 p = 0.001:0.001:0.999;
 x1 = burrinv (p, 1, 1, 1);
 x2 = burrinv (p, 1, 1, 2);
 x3 = burrinv (p, 1, 1, 3);
 x4 = burrinv (p, 1, 2, 1);
 x5 = burrinv (p, 1, 3, 1);
 x6 = burrinv (p, 1, 0.5, 2);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([0, 5])
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "northwest")
 title ("Burr type XII iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
 y = [NaN, NaN, 0, 1 , Inf, NaN, NaN];
***** assert (burrinv (p, ones (1,7), ones (1,7), ones(1,7)), y, eps)
***** assert (burrinv (p, 1, 1, 1), y, eps)
***** assert (burrinv (p, [1, 1, 1, NaN, 1, 1, 1], 1, 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv (p, 1, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv (p, 1, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv ([p, NaN], 1, 1, 1), [y, NaN], eps)
***** assert (burrinv (single ([p, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
***** error<burrinv: function called with too few input arguments.> burrinv ()
***** error<burrinv: function called with too few input arguments.> burrinv (1)
***** error<burrinv: function called with too few input arguments.> burrinv (1, 2)
***** error<burrinv: function called with too few input arguments.> burrinv (1, 2, 3)
***** error<burrinv: function called with too many inputs> ...
 burrinv (1, 2, 3, 4, 5)
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (3), ones (2), ones(2), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (3), ones(2), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (2), ones(3), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (2), ones(2), ones(3))
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (i, 2, 3, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, i, 3, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, i, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, 3, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/loglinv.m
***** demo
 ## Plot various iCDFs from the log-logistic distribution
 p = 0.001:0.001:0.999;
 x1 = loglinv (p, log (1), 1/0.5);
 x2 = loglinv (p, log (1), 1);
 x3 = loglinv (p, log (1), 1/2);
 x4 = loglinv (p, log (1), 1/4);
 x5 = loglinv (p, log (1), 1/8);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 ylim ([0, 20])
 grid on
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
 title ("Log-logistic iCDF")
 xlabel ("probability")
 ylabel ("x")
 text (0.03, 12.5, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared p, out1, out2
 p = [-1, 0, 0.2, 0.5, 0.8, 0.95, 1, 2];
 out1 = [NaN, 0, 0.25, 1, 4, 19, Inf, NaN];
 out2 = [NaN, 0, 0.0424732, 2.718282, 173.970037, 18644.695061, Inf, NaN];
***** assert (loglinv (p, 0, 1), out1, 1e-8)
***** assert (loglinv (p, 0, 1), out1, 1e-8)
***** assert (loglinv (p, 1, 3), out2, 1e-6)
***** assert (class (loglinv (single (1), 2, 3)), "single")
***** assert (class (loglinv (1, single (2), 3)), "single")
***** assert (class (loglinv (1, 2, single (3))), "single")
***** error<loglinv: function called with too few input arguments.> loglinv (1)
***** error<loglinv: function called with too few input arguments.> loglinv (1, 2)
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (1, ones (2), ones (3))
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (ones (2), 1, ones (3))
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (ones (2), ones (3), 1)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (i, 2, 3)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, i, 3)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvtpdf.m
***** demo
 ## Compute the pdf of a multivariate t distribution with correlation
 ## parameters rho = [1 .4; .4 1] and 2 degrees of freedom.

 rho = [1, 0.4; 0.4, 1];
 df = 2;
 [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
 X = [X1(:), X2(:)];
 y = mvtpdf (X, rho, df);
 surf (X1, X2, reshape (y, 25, 25));
 title ("Bivariate Student's t probability density function");
***** assert (mvtpdf ([0 0], eye(2), 1), 0.1591549, 1E-7)
***** assert (mvtpdf ([1 0], [1 0.5; 0.5 1], 2), 0.06615947, 1E-7)
***** assert (mvtpdf ([1 0.4 0; 1.2 0.5 0.5; 1.4 0.6 1], ...
 [1 0.5 0.3; 0.5 1 0.6; 0.3 0.6 1], [5 6 7]), ...
 [0.04713313 0.03722421 0.02069011]', 1E-7)
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ricernd.m
***** assert (size (ricernd (2, 1/2)), [1, 1])
***** assert (size (ricernd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (ricernd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (ricernd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (ricernd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (ricernd (ones (2, 1), 1)), [2, 1])
***** assert (size (ricernd (ones (2, 2), 1)), [2, 2])
***** assert (size (ricernd (2, 1/2, 3)), [3, 3])
***** assert (size (ricernd (1, 1, [4, 1])), [4, 1])
***** assert (size (ricernd (1, 1, 4, 1)), [4, 1])
***** assert (size (ricernd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ricernd (1, 1, 0, 1)), [0, 1])
***** assert (size (ricernd (1, 1, 1, 0)), [1, 0])
***** assert (size (ricernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (ricernd (1, 1)), "double")
***** assert (class (ricernd (1, single (0))), "single")
***** assert (class (ricernd (1, single ([0, 0]))), "single")
***** assert (class (ricernd (1, single (1), 2)), "single")
***** assert (class (ricernd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (ricernd (single (1), 1, 2)), "single")
***** assert (class (ricernd (single ([1, 1]), 1, 1, 2)), "single")
***** error<ricernd: function called with too few input arguments.> ricernd ()
***** error<ricernd: function called with too few input arguments.> ricernd (1)
***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
 ricernd (ones (3), ones (2))
***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
 ricernd (ones (2), ones (3))
***** error<ricernd: S and SIGMA must not be complex.> ricernd (i, 2)
***** error<ricernd: S and SIGMA must not be complex.> ricernd (1, i)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, -1)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, 1.2)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, ones (2))
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, [2 -1 2])
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, [2 0 2.5])
***** error<ricernd: dimensions must be non-negative integers.> ...
 ricernd (1, 1/2, 2, -1, 5)
***** error<ricernd: dimensions must be non-negative integers.> ...
 ricernd (1, 1/2, 2, 1.5, 5)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), 3)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), [3, 2])
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), 3, 2)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, 3)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, [3, 2])
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, 3, 2)
40 tests, 40 passed, 0 known failure, 0 skipped
[inst/dist_fun/invginv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/invginv.m
***** demo
 ## Plot various iCDFs from the inverse Gaussian distribution
 p = 0.001:0.001:0.999;
 x1 = invginv (p, 1, 0.2);
 x2 = invginv (p, 1, 1);
 x3 = invginv (p, 1, 3);
 x4 = invginv (p, 3, 0.2);
 x5 = invginv (p, 3, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-y")
 grid on
 ylim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northwest")
 title ("Inverse Gaussian iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p, x
 p = [0, 0.3829, 0.6827, 1];
 x = [0, 0.5207, 1.0376, Inf];
***** assert (invginv (p, 1, 1), x, 1e-4);
***** assert (invginv (p, 1, ones (1,4)), x, 1e-4);
***** assert (invginv (p, 1, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
***** assert (invginv (p, [-1, 0, 1, 1], 1), [NaN, NaN, x(3:4)], 1e-4)
***** assert (class (invginv (single ([p, NaN]), 0, 1)), "single")
***** assert (class (invginv ([p, NaN], single (0), 1)), "single")
***** assert (class (invginv ([p, NaN], 0, single (1))), "single")
***** error<invginv: function called with too few input arguments.> invginv (1)
***** error<invginv: function called with too few input arguments.> invginv (1, 2)
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (1, ones (2), ones (3))
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (ones (2), 1, ones (3))
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (ones (2), ones (3), 1)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (i, 2, 3)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, i, 3)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/gampdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gampdf.m
***** demo
 ## Plot various PDFs from the Gamma distribution
 x = 0:0.01:20;
 y1 = gampdf (x, 1, 2);
 y2 = gampdf (x, 2, 2);
 y3 = gampdf (x, 3, 2);
 y4 = gampdf (x, 5, 1);
 y5 = gampdf (x, 9, 0.5);
 y6 = gampdf (x, 7.5, 1);
 y7 = gampdf (x, 0.5, 1);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
       x, y5, "-k", x, y6, "-b", x, y7, "-c")
 grid on
 ylim ([0,0.5])
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "northeast")
 title ("Gamma PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0 exp(-x(2:end))];
***** assert (gampdf (x, ones (1,5), ones (1,5)), y)
***** assert (gampdf (x, 1, ones (1,5)), y)
***** assert (gampdf (x, ones (1,5), 1), y)
***** assert (gampdf (x, [0 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN y(5)])
***** assert (gampdf (x, 1, [0 -Inf NaN Inf 1]), [NaN NaN NaN 0 y(5)])
***** assert (gampdf ([x, NaN], 1, 1), [y, NaN])
***** assert (gampdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (gampdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (gampdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<gampdf: function called with too few input arguments.> gampdf ()
***** error<gampdf: function called with too few input arguments.> gampdf (1)
***** error<gampdf: function called with too few input arguments.> gampdf (1,2)
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (3), ones (2), ones (2))
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (2), ones (3), ones (2))
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (2), ones (2), ones (3))
***** error<gampdf: X, A, and B must not be complex.> gampdf (i, 2, 2)
***** error<gampdf: X, A, and B must not be complex.> gampdf (2, i, 2)
***** error<gampdf: X, A, and B must not be complex.> gampdf (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wblinv.m
***** demo
 ## Plot various iCDFs from the Weibull distribution
 p = 0.001:0.001:0.999;
 x1 = wblinv (p, 1, 0.5);
 x2 = wblinv (p, 1, 1);
 x3 = wblinv (p, 1, 1.5);
 x4 = wblinv (p, 1, 5);
 plot (p, x1, "-b", p, x2, "-r", p, x3, "-m", p, x4, "-g")
 ylim ([0, 2.5])
 grid on
 legend ({"λ = 1, k = 0.5", "λ = 1, k = 1",  ...
          "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "northwest")
 title ("Weibull iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p
 p = [-1 0 0.63212055882855778 1 2];
***** assert (wblinv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, [1 -1 NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (wblinv (p, 1, [1 -1 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (wblinv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (wblinv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
***** assert (wblinv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** assert (wblinv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** assert (wblinv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** error<wblinv: invalid number of input arguments.> wblinv ()
***** error<wblinv: invalid number of input arguments.> wblinv (1,2,3,4)
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (3), ones (2), ones (2))
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (2), ones (3), ones (2))
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (2), ones (2), ones (3))
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (i, 2, 2)
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, i, 2)
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/vminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/vminv.m
***** demo
 ## Plot various iCDFs from the von Mises distribution
 p1 = [0,0.005,0.01:0.01:0.1,0.15,0.2:0.1:0.8,0.85,0.9:0.01:0.99,0.995,1];
 x1 = vminv (p1, 0, 0.5);
 x2 = vminv (p1, 0, 1);
 x3 = vminv (p1, 0, 2);
 x4 = vminv (p1, 0, 4);
 plot (p1, x1, "-r", p1, x2, "-g", p1, x3, "-b", p1, x4, "-c")
 grid on
 ylim ([-pi, pi])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared x, p0, p1
 x = [-pi:pi/2:pi];
 p0 = [0, 0.10975, 0.5, 0.89025, 1];
 p1 = [0, 0.03752, 0.5, 0.99622, 1];
***** assert (vminv (p0, 0, 1), x, 5e-5)
***** assert (vminv (p0, zeros (1,5), ones (1,5)), x, 5e-5)
***** assert (vminv (p1, 0, [1 2 3 4 5]), x, [5e-5, 5e-4, 5e-5, 5e-4, 5e-5])
***** error<vminv: function called with too few input arguments.> vminv ()
***** error<vminv: function called with too few input arguments.> vminv (1)
***** error<vminv: function called with too few input arguments.> vminv (1, 2)
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (3), ones (2), ones (2))
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (2), ones (3), ones (2))
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (2), ones (2), ones (3))
***** error<vminv: P, MU, and K must not be complex.> vminv (i, 2, 2)
***** error<vminv: P, MU, and K must not be complex.> vminv (2, i, 2)
***** error<vminv: P, MU, and K must not be complex.> vminv (2, 2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bisarnd.m
***** assert (size (bisarnd (1, 1)), [1 1])
***** assert (size (bisarnd (1, ones (2,1))), [2, 1])
***** assert (size (bisarnd (1, ones (2,2))), [2, 2])
***** assert (size (bisarnd (ones (2,1), 1)), [2, 1])
***** assert (size (bisarnd (ones (2,2), 1)), [2, 2])
***** assert (size (bisarnd (1, 1, 3)), [3, 3])
***** assert (size (bisarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (bisarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (bisarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (bisarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (bisarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (bisarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (bisarnd (1, 1)), "double")
***** assert (class (bisarnd (1, single (1))), "single")
***** assert (class (bisarnd (1, single ([1, 1]))), "single")
***** assert (class (bisarnd (single (1), 1)), "single")
***** assert (class (bisarnd (single ([1, 1]), 1)), "single")
***** error<bisarnd: function called with too few input arguments.> bisarnd ()
***** error<bisarnd: function called with too few input arguments.> bisarnd (1)
***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
 bisarnd (ones (3), ones (2))
***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
 bisarnd (ones (2), ones (3))
***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (i, 2, 3)
***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (1, i, 3)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, -1)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, 1.2)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, ones (2))
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, [2 -1 2])
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, [2 0 2.5])
***** error<bisarnd: dimensions must be non-negative integers.> ...
 bisarnd (1, 2, 2, -1, 5)
***** error<bisarnd: dimensions must be non-negative integers.> ...
 bisarnd (1, 2, 2, 1.5, 5)
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), 3)
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), [3, 2])
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/tripdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tripdf.m
***** demo
 ## Plot various CDFs from the triangular distribution
 x = 0.001:0.001:10;
 y1 = tripdf (x, 3, 4, 6);
 y2 = tripdf (x, 1, 2, 5);
 y3 = tripdf (x, 2, 3, 9);
 y4 = tripdf (x, 2, 5, 9);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
          "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
         "location", "northeast")
 title ("Triangular CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, deps
 x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
 y = [0, 0, 0.4, 2, 0.4, 0, 0];
 deps = 2*eps;
***** assert (tripdf (x, ones (1,7), 1.5*ones (1,7), 2*ones (1,7)), y, deps)
***** assert (tripdf (x, 1*ones (1,7), 1.5, 2), y, deps)
***** assert (tripdf (x, 1, 1.5, 2*ones (1,7)), y, deps)
***** assert (tripdf (x, 1, 1.5*ones (1,7), 2), y, deps)
***** assert (tripdf (x, 1, 1.5, 2), y, deps)
***** assert (tripdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf (x, 1, 1.5*[1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf ([x, NaN], 1, 1.5, 2), [y, NaN], deps)
***** assert (tripdf (single ([x, NaN]), 1, 1.5, 2), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], single (1), 1.5, 2), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], 1, single (1.5), 2), single ([y, NaN]), eps("single"))
***** error<tripdf: function called with too few input arguments.> tripdf ()
***** error<tripdf: function called with too few input arguments.> tripdf (1)
***** error<tripdf: function called with too few input arguments.> tripdf (1, 2)
***** error<tripdf: function called with too few input arguments.> tripdf (1, 2, 3)
***** error<tripdf: function called with too many inputs> ...
 tripdf (1, 2, 3, 4, 5)
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (3), ones (2), ones(2), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (3), ones(2), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (2), ones(3), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (2), ones(2), ones(3))
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (i, 2, 3, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, i, 3, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, i, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, 3, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/raylpdf.m
***** demo
 ## Plot various PDFs from the Rayleigh distribution
 x = 0:0.01:10;
 y1 = raylpdf (x, 0.5);
 y2 = raylpdf (x, 1);
 y3 = raylpdf (x, 2);
 y4 = raylpdf (x, 3);
 y5 = raylpdf (x, 4);
 plot (x, y1, "-b", x, y2, "g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
 grid on
 ylim ([0, 1.25])
 legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "northeast")
 title ("Rayleigh PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 y = raylpdf (x, sigma);
 expected_y = [0.0000, 0.1212, 0.1051, 0.0874, 0.0738, 0.0637];
 assert (y, expected_y, 0.001);
***** test
 x = 0:0.5:2.5;
 y = raylpdf (x, 0.5);
 expected_y = [0.0000, 1.2131, 0.5413, 0.0667, 0.0027, 0.0000];
 assert (y, expected_y, 0.001);
***** error<raylpdf: function called with too few input arguments.> raylpdf ()
***** error<raylpdf: function called with too few input arguments.> raylpdf (1)
***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
 raylpdf (ones (3), ones (2))
***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
 raylpdf (ones (2), ones (3))
***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (i, 2)
***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/raylrnd.m
***** assert (size (raylrnd (2)), [1, 1])
***** assert (size (raylrnd (ones (2,1))), [2, 1])
***** assert (size (raylrnd (ones (2,2))), [2, 2])
***** assert (size (raylrnd (1, 3)), [3, 3])
***** assert (size (raylrnd (1, [4 1])), [4, 1])
***** assert (size (raylrnd (1, 4, 1)), [4, 1])
***** assert (size (raylrnd (1, 4, 1)), [4, 1])
***** assert (size (raylrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (raylrnd (1, 0, 1)), [0, 1])
***** assert (size (raylrnd (1, 1, 0)), [1, 0])
***** assert (size (raylrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (raylrnd (0, 1, 1), NaN)
***** assert (raylrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (raylrnd (2)), "double")
***** assert (class (raylrnd (single (2))), "single")
***** assert (class (raylrnd (single ([2 2]))), "single")
***** error<raylrnd: function called with too few input arguments.> raylrnd ()
***** error<raylrnd: SIGMA must not be complex.> raylrnd (i)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, -1)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, 1.2)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, ones (2))
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, [2 -1 2])
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, [2 0 2.5])
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (ones (2), ones (2))
***** error<raylrnd: dimensions must be non-negative integers.> ...
 raylrnd (1, 2, -1, 5)
***** error<raylrnd: dimensions must be non-negative integers.> ...
 raylrnd (1, 2, 1.5, 5)
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 3)
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), [3, 2])
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/fcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/fcdf.m
***** demo
 ## Plot various CDFs from the F distribution
 x = 0.01:0.01:4;
 p1 = fcdf (x, 1, 2);
 p2 = fcdf (x, 2, 1);
 p3 = fcdf (x, 5, 2);
 p4 = fcdf (x, 10, 1);
 p5 = fcdf (x, 100, 100);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "southeast")
 title ("F CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 0.5, 1, 2, Inf];
 y = [0, 0, 1/3, 1/2, 2/3, 1];
***** assert (fcdf (x, 2*ones (1,6), 2*ones (1,6)), y, eps)
***** assert (fcdf (x, 2, 2*ones (1,6)), y, eps)
***** assert (fcdf (x, 2*ones (1,6), 2), y, eps)
***** assert (fcdf (x, [0 NaN Inf 2 2 2], 2), [NaN NaN 0.1353352832366127 y(4:6)], eps)
***** assert (fcdf (x, 2, [0 NaN Inf 2 2 2]), [NaN NaN 0.3934693402873666 y(4:6)], eps)
***** assert (fcdf ([x(1:2) NaN x(4:6)], 2, 2), [y(1:2) NaN y(4:6)], eps)
***** assert (fcdf ([x, NaN], 2, 2), [y, NaN], eps)
***** assert (fcdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
***** assert (fcdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
***** assert (fcdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
***** error<fcdf: function called with too few input arguments.> fcdf ()
***** error<fcdf: function called with too few input arguments.> fcdf (1)
***** error<fcdf: function called with too few input arguments.> fcdf (1, 2)
***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, 4)
***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, "tail")
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (3), ones (2), ones (2))
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (2), ones (3), ones (2))
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (2), ones (2), ones (3))
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (i, 2, 2)
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, i, 2)
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, 2, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/normcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/normcdf.m
***** demo
 ## Plot various CDFs from the normal distribution
 x = -5:0.01:5;
 p1 = normcdf (x, 0, 0.5);
 p2 = normcdf (x, 0, 1);
 p3 = normcdf (x, 0, 2);
 p4 = normcdf (x, -2, 0.8);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-5, 5])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "southeast")
 title ("Normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf 1 2 Inf];
 y = [0, 0.5, 1/2*(1+erf(1/sqrt(2))), 1];
***** assert (normcdf (x, ones (1,4), ones (1,4)), y)
***** assert (normcdf (x, 1, ones (1,4)), y)
***** assert (normcdf (x, ones (1,4), 1), y)
***** assert (normcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN])
***** assert (normcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, 1])
***** assert (normcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)])
***** assert (normcdf (x, "upper"), [1, 0.1587, 0.0228, 0], 1e-4)
***** assert (normcdf ([x, NaN], 1, 1), [y, NaN])
***** assert (normcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (normcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (normcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<normcdf: invalid number of input arguments.> normcdf ()
***** error<normcdf: invalid number of input arguments.> normcdf (1,2,3,4,5,6,7)
***** error<normcdf: invalid argument for upper tail.> normcdf (1, 2, 3, 4, "uper")
***** error<normcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normcdf (ones (3), ones (2), ones (2))
***** error<normcdf: invalid size of covariance matrix.> normcdf (2, 3, 4, [1, 2])
***** error<normcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = normcdf (1, 2, 3)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (i, 2, 2)
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, i, 2)
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, 2, i)
***** error<normcdf: bad covariance matrix.> ...
 [p, plo, pup] =normcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gevinv.m
***** demo
 ## Plot various iCDFs from the generalized extreme value distribution
 p = 0.001:0.001:0.999;
 x1 = gevinv (p, 1, 1, 1);
 x2 = gevinv (p, 0.5, 1, 1);
 x3 = gevinv (p, 1, 1, 5);
 x4 = gevinv (p, 1, 2, 5);
 x5 = gevinv (p, 1, 5, 5);
 x6 = gevinv (p, 1, 0.5, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([-1, 10])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "northwest")
 title ("Generalized extreme value iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 p = 0.1:0.1:0.9;
 k = 0;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** test
 p = 0.1:0.1:0.9;
 k = 1;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** test
 p = 0.1:0.1:0.9;
 k = 0.3;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** error<gevinv: function called with too few input arguments.> gevinv ()
***** error<gevinv: function called with too few input arguments.> gevinv (1)
***** error<gevinv: function called with too few input arguments.> gevinv (1, 2)
***** error<gevinv: function called with too few input arguments.> gevinv (1, 2, 3)
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (3), ones (2), ones(2), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (3), ones(2), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (2), ones(3), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (2), ones(2), ones(3))
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (i, 2, 3, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, i, 3, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, i, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, 3, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/evpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/evpdf.m
***** demo
 ## Plot various PDFs from the Extreme value distribution
 x = -10:0.001:10;
 y1 = evpdf (x, 0.5, 2);
 y2 = evpdf (x, 1.0, 2);
 y3 = evpdf (x, 1.5, 3);
 y4 = evpdf (x, 3.0, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 ylim ([0, 0.2])
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northeast")
 title ("Extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [0.0067, 0.3679, 0.1794, 0.0046, 0];
 y1 = [0.0025, 0.2546, 0.3679, 0.1794, 0.0046];
***** assert (evpdf (x), y0, 1e-4)
***** assert (evpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
***** assert (evpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
***** error<evpdf: function called with too few input arguments.> evpdf ()
***** error<evpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 evpdf (ones (3), ones (2), ones (2))
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (i, 2, 2)
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, i, 2)
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, 2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unidrnd.m
***** assert (size (unidrnd (2)), [1, 1])
***** assert (size (unidrnd (ones (2,1))), [2, 1])
***** assert (size (unidrnd (ones (2,2))), [2, 2])
***** assert (size (unidrnd (1, 3)), [3, 3])
***** assert (size (unidrnd (1, [4 1])), [4, 1])
***** assert (size (unidrnd (1, 4, 1)), [4, 1])
***** assert (size (unidrnd (1, 4, 1)), [4, 1])
***** assert (size (unidrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (unidrnd (1, 0, 1)), [0, 1])
***** assert (size (unidrnd (1, 1, 0)), [1, 0])
***** assert (size (unidrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (unidrnd (0, 1, 1), NaN)
***** assert (unidrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (unidrnd (2)), "double")
***** assert (class (unidrnd (single (2))), "single")
***** assert (class (unidrnd (single ([2 2]))), "single")
***** error<unidrnd: function called with too few input arguments.> unidrnd ()
***** error<unidrnd: N must not be complex.> unidrnd (i)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, -1)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, 1.2)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, ones (2))
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, [2 -1 2])
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, [2 0 2.5])
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (ones (2), ones (2))
***** error<unidrnd: dimensions must be non-negative integers.> ...
 unidrnd (1, 2, -1, 5)
***** error<unidrnd: dimensions must be non-negative integers.> ...
 unidrnd (1, 2, 1.5, 5)
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 3)
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), [3, 2])
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/hninv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hninv.m
***** demo
 ## Plot various iCDFs from the half-normal distribution
 p = 0.001:0.001:0.999;
 x1 = hninv (p, 0, 1);
 x2 = hninv (p, 0, 2);
 x3 = hninv (p, 0, 3);
 x4 = hninv (p, 0, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([0, 10])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northwest")
 title ("Half-normal iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p, x
 p = [0, 0.3829, 0.6827, 1];
 x = [0, 1/2, 1, Inf];
***** assert (hninv (p, 0, 1), x, 1e-4);
***** assert (hninv (p, 5, 1), x + 5, 1e-4);
***** assert (hninv (p, 0, ones (1,4)), x, 1e-4);
***** assert (hninv (p, 0, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
***** assert (class (hninv (single ([p, NaN]), 0, 1)), "single")
***** assert (class (hninv ([p, NaN], single (0), 1)), "single")
***** assert (class (hninv ([p, NaN], 0, single (1))), "single")
***** error<hninv: function called with too few input arguments.> hninv (1)
***** error<hninv: function called with too few input arguments.> hninv (1, 2)
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (1, ones (2), ones (3))
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (ones (2), 1, ones (3))
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (ones (2), ones (3), 1)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (i, 2, 3)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, i, 3)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/hncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hncdf.m
***** demo
 ## Plot various CDFs from the half-normal distribution
 x = 0:0.001:10;
 p1 = hncdf (x, 0, 1);
 p2 = hncdf (x, 0, 2);
 p3 = hncdf (x, 0, 3);
 p4 = hncdf (x, 0, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "southeast")
 title ("Half-normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot half-normal against normal cumulative distribution function
 x = -5:0.001:5;
 p1 = hncdf (x, 0, 1);
 p2 = normcdf (x);
 plot (x, p1, "-b", x, p2, "-g")
 grid on
 xlim ([-5, 5])
 legend ({"half-normal with μ = 0, σ = 1", ...
          "standart normal (μ = 0, σ = 1)"}, "location", "southeast")
 title ("Half-normal against standard normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p1, p1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 p1 = [0, 0, 0, 0.3829, 0.6827, 1];
 p1u = [1, 1, 1, 0.6171, 0.3173, 0];
***** assert (hncdf (x, zeros (1,6), ones (1,6)), p1, 1e-4)
***** assert (hncdf (x, 0, 1), p1, 1e-4)
***** assert (hncdf (x, 0, ones (1,6)), p1, 1e-4)
***** assert (hncdf (x, zeros (1,6), 1), p1, 1e-4)
***** assert (hncdf (x, 0, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf (x, [0, 0, 0, NaN, 0, 0], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf ([x(1:3), NaN, x(5:6)], 0, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf (x, zeros (1,6), ones (1,6), "upper"), p1u, 1e-4)
***** assert (hncdf (x, 0, 1, "upper"), p1u, 1e-4)
***** assert (hncdf (x, 0, ones (1,6), "upper"), p1u, 1e-4)
***** assert (hncdf (x, zeros (1,6), 1, "upper"), p1u, 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
***** error<hncdf: function called with too few input arguments.> hncdf ()
***** error<hncdf: function called with too few input arguments.> hncdf (1)
***** error<hncdf: function called with too few input arguments.> hncdf (1, 2)
***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, "tail")
***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, 5)
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (3), ones (2), ones(2))
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (2), ones (3), ones(2))
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (2), ones (2), ones(3))
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (i, 2, 3)
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, i, 3)
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/poisspdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/poisspdf.m
***** demo
 ## Plot various PDFs from the Poisson distribution
 x = 0:20;
 y1 = poisspdf (x, 1);
 y2 = poisspdf (x, 4);
 y3 = poisspdf (x, 10);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.4])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northeast")
 title ("Poisson PDF")
 xlabel ("values in x (number of occurences)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0, exp(-1)*[1 1 0.5], 0];
***** assert (poisspdf (x, ones (1,5)), y, eps)
***** assert (poisspdf (x, 1), y, eps)
***** assert (poisspdf (x, [1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)], eps)
***** assert (poisspdf ([x, NaN], 1), [y, NaN], eps)
***** assert (poisspdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (poisspdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<poisspdf: function called with too few input arguments.> poisspdf ()
***** error<poisspdf: function called with too few input arguments.> poisspdf (1)
***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
 poisspdf (ones (3), ones (2))
***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
 poisspdf (ones (2), ones (3))
***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (i, 2)
***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/riceinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/riceinv.m
***** demo
 ## Plot various iCDFs from the Rician distribution
 p = 0.001:0.001:0.999;
 x1 = riceinv (p, 0, 1);
 x2 = riceinv (p, 0.5, 1);
 x3 = riceinv (p, 1, 1);
 x4 = riceinv (p, 2, 1);
 x5 = riceinv (p, 4, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
 grid on
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northwest")
 title ("Rician iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (riceinv (p, ones (1,5), 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, 1, 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, ones (1,5), 2), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, [1 0 NaN 1 1], 2), [NaN 0 NaN Inf NaN])
***** assert (riceinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
***** assert (riceinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
***** assert (riceinv ([p, NaN], 1, 2), [NaN 0 3.5354 Inf NaN NaN], 1e-4)
***** assert (riceinv (single ([p, NaN]), 1, 2), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** assert (riceinv ([p, NaN], single (1), 2), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** assert (riceinv ([p, NaN], 1, single (2)), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** error<riceinv: function called with too few input arguments.> riceinv ()
***** error<riceinv: function called with too few input arguments.> riceinv (1)
***** error<riceinv: function called with too few input arguments.> riceinv (1,2)
***** error<riceinv: function called with too many inputs> riceinv (1,2,3,4)
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (3), ones (2), ones (2))
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (2), ones (3), ones (2))
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (2), ones (2), ones (3))
***** error<riceinv: P, S, and B must not be complex.> riceinv (i, 2, 2)
***** error<riceinv: P, S, and B must not be complex.> riceinv (2, i, 2)
***** error<riceinv: P, S, and B must not be complex.> riceinv (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/invgcdf.m
***** demo
 ## Plot various CDFs from the inverse Gaussian distribution
 x = 0:0.001:3;
 p1 = invgcdf (x, 1, 0.2);
 p2 = invgcdf (x, 1, 1);
 p3 = invgcdf (x, 1, 3);
 p4 = invgcdf (x, 3, 0.2);
 p5 = invgcdf (x, 3, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-y")
 grid on
 xlim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "southeast")
 title ("Inverse Gaussian CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p1, p1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 p1 = [0, 0, 0, 0.3650, 0.6681, 1];
 p1u = [1, 1, 1, 0.6350, 0.3319, 0];
***** assert (invgcdf (x, ones (1,6), ones (1,6)), p1, 1e-4)
***** assert (invgcdf (x, 1, 1), p1, 1e-4)
***** assert (invgcdf (x, 1, ones (1,6)), p1, 1e-4)
***** assert (invgcdf (x, ones (1,6), 1), p1, 1e-4)
***** assert (invgcdf (x, 1, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf (x, [1, 1, 1, NaN, 1, 1], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf ([x(1:3), NaN, x(5:6)], 1, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf (x, ones (1,6), ones (1,6), "upper"), p1u, 1e-4)
***** assert (invgcdf (x, 1, 1, "upper"), p1u, 1e-4)
***** assert (invgcdf (x, 1, ones (1,6), "upper"), p1u, 1e-4)
***** assert (invgcdf (x, ones (1,6), 1, "upper"), p1u, 1e-4)
***** assert (class (invgcdf (single ([x, NaN]), 1, 1)), "single")
***** assert (class (invgcdf ([x, NaN], 1, single (1))), "single")
***** assert (class (invgcdf ([x, NaN], single (1), 1)), "single")
***** error<invgcdf: function called with too few input arguments.> invgcdf ()
***** error<invgcdf: function called with too few input arguments.> invgcdf (1)
***** error<invgcdf: function called with too few input arguments.> invgcdf (1, 2)
***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, "tail")
***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, 5)
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (3), ones (2), ones(2))
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (2), ones (3), ones(2))
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (2), ones (2), ones(3))
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (i, 2, 3)
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, i, 3)
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplaceinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/laplaceinv.m
***** demo
 ## Plot various iCDFs from the Laplace distribution
 p = 0.001:0.001:0.999;
 x1 = cauchyinv (p, 0, 1);
 x2 = cauchyinv (p, 0, 2);
 x3 = cauchyinv (p, 0, 4);
 x4 = cauchyinv (p, -5, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-10, 10])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northwest")
 title ("Laplace iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [-1 0 0.5 1 2];
 x = [NaN, -Inf, 0, Inf, NaN];
***** assert (laplaceinv (p, 0, 1), x)
***** assert (laplaceinv (p, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), Inf, NaN])
***** assert (laplaceinv ([p, NaN], 0, 1), [x, NaN])
***** assert (laplaceinv (single ([p, NaN]), 0, 1), single ([x, NaN]))
***** assert (laplaceinv ([p, NaN], single (0), 1), single ([x, NaN]))
***** assert (laplaceinv ([p, NaN], 0, single (1)), single ([x, NaN]))
***** error<laplaceinv: function called with too few input arguments.> laplaceinv ()
***** error<laplaceinv: function called with too few input arguments.> laplaceinv (1)
***** error<laplaceinv: function called with too few input arguments.> ...
 laplaceinv (1, 2)
***** error<laplaceinv: function called with too many inputs> laplaceinv (1, 2, 3, 4)
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (1, ones (2), ones (3))
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (ones (2), 1, ones (3))
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (ones (2), ones (3), 1)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (i, 2, 3)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, i, 3)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bisapdf.m
***** demo
 ## Plot various PDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:4;
 y1 = bisapdf (x, 1, 0.5);
 y2 = bisapdf (x, 1, 1);
 y3 = bisapdf (x, 1, 2);
 y4 = bisapdf (x, 1, 5);
 y5 = bisapdf (x, 1, 10);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 1.5])
 legend ({"β = 1 ,γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northeast")
 title ("Birnbaum-Saunders PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Plot various PDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:6;
 y1 = bisapdf (x, 1, 0.3);
 y2 = bisapdf (x, 2, 0.3);
 y3 = bisapdf (x, 1, 0.5);
 y4 = bisapdf (x, 3, 0.5);
 y5 = bisapdf (x, 5, 0.5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 1.5])
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.3989422804014327, 0.1647717335503959, 0];
***** assert (bisapdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (bisapdf (x, 1, 1), y, eps)
***** assert (bisapdf (x, 1, ones (1,5)), y, eps)
***** assert (bisapdf (x, ones (1,5), 1), y, eps)
***** assert (bisapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisapdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (bisapdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisapdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisapdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisapdf: function called with too few input arguments.> bisapdf ()
***** error<bisapdf: function called with too few input arguments.> bisapdf (1)
***** error<bisapdf: function called with too few input arguments.> bisapdf (1, 2)
***** error<bisapdf: function called with too many inputs> bisapdf (1, 2, 3, 4)
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (3), ones (2), ones(2))
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (2), ones (3), ones(2))
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (2), ones (2), ones(3))
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (i, 4, 3)
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, i, 3)
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/trirnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/trirnd.m
***** assert (size (trirnd (1, 1.5, 2)), [1, 1])
***** assert (size (trirnd (1 * ones (2, 1), 1.5, 2)), [2, 1])
***** assert (size (trirnd (1 * ones (2, 2), 1.5, 2)), [2, 2])
***** assert (size (trirnd (1, 1.5 * ones (2, 1), 2)), [2, 1])
***** assert (size (trirnd (1, 1.5 * ones (2, 2), 2)), [2, 2])
***** assert (size (trirnd (1, 1.5, 2 * ones (2, 1))), [2, 1])
***** assert (size (trirnd (1, 1.5, 2 * ones (2, 2))), [2, 2])
***** assert (size (trirnd (1, 1.5, 2, 3)), [3, 3])
***** assert (size (trirnd (1, 1.5, 2, [4, 1])), [4, 1])
***** assert (size (trirnd (1, 1.5, 2, 4, 1)), [4, 1])
***** assert (class (trirnd (1, 1.5, 2)), "double")
***** assert (class (trirnd (single (1), 1.5, 2)), "single")
***** assert (class (trirnd (single ([1, 1]), 1.5, 2)), "single")
***** assert (class (trirnd (1, single (1.5), 2)), "single")
***** assert (class (trirnd (1, single ([1.5, 1.5]), 2)), "single")
***** assert (class (trirnd (1, 1.5, single (1.5))), "single")
***** assert (class (trirnd (1, 1.5, single ([2, 2]))), "single")
***** error<trirnd: function called with too few input arguments.> trirnd ()
***** error<trirnd: function called with too few input arguments.> trirnd (1)
***** error<trirnd: function called with too few input arguments.> trirnd (1, 2)
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (3), 5 * ones (2), ones (2))
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (2), 5 * ones (3), ones (2))
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (2), 5 * ones (2), ones (3))
***** error<trirnd: A, B, and C must not be complex.> trirnd (i, 5, 3)
***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5+i, 3)
***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5, i)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, -1)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, 1.2)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, ones (2))
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, [2 -1 2])
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, [2 0 2.5])
***** error<trirnd: dimensions must be non-negative integers.> ...
 trirnd (1, 5, 3, 2, -1, 5)
***** error<trirnd: dimensions must be non-negative integers.> ...
 trirnd (1, 5, 3, 2, 1.5, 5)
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, 3)
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, [3, 2])
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, 3, 2)
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_fun/evrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/evrnd.m
***** assert (size (evrnd (1, 1)), [1 1])
***** assert (size (evrnd (1, ones (2,1))), [2, 1])
***** assert (size (evrnd (1, ones (2,2))), [2, 2])
***** assert (size (evrnd (ones (2,1), 1)), [2, 1])
***** assert (size (evrnd (ones (2,2), 1)), [2, 2])
***** assert (size (evrnd (1, 1, 3)), [3, 3])
***** assert (size (evrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (evrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (evrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (evrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (evrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (evrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (evrnd (1, 1)), "double")
***** assert (class (evrnd (1, single (1))), "single")
***** assert (class (evrnd (1, single ([1, 1]))), "single")
***** assert (class (evrnd (single (1), 1)), "single")
***** assert (class (evrnd (single ([1, 1]), 1)), "single")
***** error<evrnd: function called with too few input arguments.> evrnd ()
***** error<evrnd: function called with too few input arguments.> evrnd (1)
***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
 evrnd (ones (3), ones (2))
***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
 evrnd (ones (2), ones (3))
***** error<evrnd: MU and SIGMA must not be complex.> evrnd (i, 2, 3)
***** error<evrnd: MU and SIGMA must not be complex.> evrnd (1, i, 3)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, -1)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, 1.2)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, ones (2))
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, [2 -1 2])
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, [2 0 2.5])
***** error<evrnd: dimensions must be non-negative integers.> ...
 evrnd (1, 2, 2, -1, 5)
***** error<evrnd: dimensions must be non-negative integers.> ...
 evrnd (1, 2, 2, 1.5, 5)
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), 3)
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), [3, 2])
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/betarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/betarnd.m
***** assert (size (betarnd (2, 1/2)), [1 1])
***** assert (size (betarnd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (betarnd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (betarnd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (betarnd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (betarnd (ones (2, 1), 1)), [2, 1])
***** assert (size (betarnd (ones (2, 2), 1)), [2, 2])
***** assert (size (betarnd (2, 1/2, 3)), [3, 3])
***** assert (size (betarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (betarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (betarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (betarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (betarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (betarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (betarnd (1, 1)), "double")
***** assert (class (betarnd (1, single (0))), "single")
***** assert (class (betarnd (1, single ([0, 0]))), "single")
***** assert (class (betarnd (1, single (1), 2)), "single")
***** assert (class (betarnd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (betarnd (single (1), 1, 2)), "single")
***** assert (class (betarnd (single ([1, 1]), 1, 1, 2)), "single")
***** error<betarnd: function called with too few input arguments.> betarnd ()
***** error<betarnd: function called with too few input arguments.> betarnd (1)
***** error<betarnd: A and B must be of common size or scalars.> ...
 betarnd (ones (3), ones (2))
***** error<betarnd: A and B must be of common size or scalars.> ...
 betarnd (ones (2), ones (3))
***** error<betarnd: A and B must not be complex.> betarnd (i, 2)
***** error<betarnd: A and B must not be complex.> betarnd (1, i)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, -1)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, 1.2)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, ones (2))
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, [2 -1 2])
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, [2 0 2.5])
***** error<betarnd: dimensions must be non-negative integers.> ...
 betarnd (1, 1/2, 2, -1, 5)
***** error<betarnd: dimensions must be non-negative integers.> ...
 betarnd (1, 1/2, 2, 1.5, 5)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), 3)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), [3, 2])
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), 3, 2)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, 3)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, [3, 2])
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, 3, 2)
40 tests, 40 passed, 0 known failure, 0 skipped
[inst/dist_fun/gaminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gaminv.m
***** demo
 ## Plot various iCDFs from the Gamma distribution
 p = 0.001:0.001:0.999;
 x1 = gaminv (p, 1, 2);
 x2 = gaminv (p, 2, 2);
 x3 = gaminv (p, 3, 2);
 x4 = gaminv (p, 5, 1);
 x5 = gaminv (p, 9, 0.5);
 x6 = gaminv (p, 7.5, 1);
 x7 = gaminv (p, 0.5, 1);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
       p, x5, "-k", p, x6, "-b", p, x7, "-c")
 ylim ([0, 20])
 grid on
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "northwest")
 title ("Gamma iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p
 p = [-1 0 0.63212055882855778 1 2];
***** assert (gaminv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (gaminv (p, 1, [1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (gaminv (1e-16, 1, 1), 1e-16, eps)
***** assert (gaminv (1e-16, 1, 2), 2e-16, eps)
***** assert (gaminv (1e-20, 3, 5), 1.957434012161815e-06, eps)
***** assert (gaminv (1e-15, 1, 1), 1e-15, eps)
***** assert (gaminv (1e-35, 1, 1), 1e-35, eps)
***** assert (gaminv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
***** assert (gaminv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** assert (gaminv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** assert (gaminv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** error<gaminv: function called with too few input arguments.> gaminv ()
***** error<gaminv: function called with too few input arguments.> gaminv (1)
***** error<gaminv: function called with too few input arguments.> gaminv (1,2)
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (3), ones (2), ones (2))
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (2), ones (3), ones (2))
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (2), ones (2), ones (3))
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (i, 2, 2)
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, i, 2)
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unifinv.m
***** demo
 ## Plot various iCDFs from the continuous uniform distribution
 p = 0.001:0.001:0.999;
 x1 = unifinv (p, 2, 5);
 x2 = unifinv (p, 3, 9);
 plot (p, x1, "-b", p, x2, "-g")
 grid on
 xlim ([0, 1])
 ylim ([0, 10])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northwest")
 title ("Continuous uniform iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (unifinv (p, ones (1,5), 2*ones (1,5)), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, 0, 1), [NaN 1 1.5 2 NaN] - 1)
***** assert (unifinv (p, 1, 2*ones (1,5)), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, ones (1,5), 2), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, [1 2 NaN 1 1], 2), [NaN NaN NaN 2 NaN])
***** assert (unifinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 2 NaN])
***** assert (unifinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 1 NaN 2 NaN])
***** assert (unifinv ([p, NaN], 1, 2), [NaN 1 1.5 2 NaN NaN])
***** assert (unifinv (single ([p, NaN]), 1, 2), single ([NaN 1 1.5 2 NaN NaN]))
***** assert (unifinv ([p, NaN], single (1), 2), single ([NaN 1 1.5 2 NaN NaN]))
***** assert (unifinv ([p, NaN], 1, single (2)), single ([NaN 1 1.5 2 NaN NaN]))
***** error<unifinv: function called with too few input arguments.> unifinv ()
***** error<unifinv: function called with too few input arguments.> unifinv (1, 2)
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (3), ones (2), ones (2))
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (2), ones (3), ones (2))
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (2), ones (2), ones (3))
***** error<unifinv: P, A, and B must not be complex.> unifinv (i, 2, 2)
***** error<unifinv: P, A, and B must not be complex.> unifinv (2, i, 2)
***** error<unifinv: P, A, and B must not be complex.> unifinv (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchyinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/cauchyinv.m
***** demo
 ## Plot various iCDFs from the Cauchy distribution
 p = 0.001:0.001:0.999;
 x1 = cauchyinv (p, 0, 0.5);
 x2 = cauchyinv (p, 0, 1);
 x3 = cauchyinv (p, 0, 2);
 x4 = cauchyinv (p, -2, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 5])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northwest")
 title ("Cauchy iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (cauchyinv (p, ones (1,5), 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, 1, 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, ones (1,5), 2), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, [1 -Inf NaN Inf 1], 2), [NaN NaN NaN NaN NaN])
***** assert (cauchyinv (p, 1, 2 * [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (cauchyinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN -Inf NaN Inf NaN])
***** assert (cauchyinv ([p, NaN], 1, 2), [NaN -Inf 1 Inf NaN NaN], eps)
***** assert (cauchyinv (single ([p, NaN]), 1, 2), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** assert (cauchyinv ([p, NaN], single (1), 2), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** assert (cauchyinv ([p, NaN], 1, single (2)), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** error<cauchyinv: function called with too few input arguments.> cauchyinv ()
***** error<cauchyinv: function called with too few input arguments.> cauchyinv (1)
***** error<cauchyinv: function called with too few input arguments.> ...
 cauchyinv (1, 2)
***** error<cauchyinv: function called with too many inputs> cauchyinv (1, 2, 3, 4)
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (3), ones (2), ones(2))
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (2), ones (3), ones(2))
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (2), ones (2), ones(3))
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (i, 4, 3)
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, i, 3)
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/hnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hnrnd.m
***** assert (size (hnrnd (1, 1, 1)), [1, 1])
***** assert (size (hnrnd (1, 1, 2)), [2, 2])
***** assert (size (hnrnd (1, 1, [2, 1])), [2, 1])
***** assert (size (hnrnd (1, zeros (2, 2))), [2, 2])
***** assert (size (hnrnd (1, ones (2, 1))), [2, 1])
***** assert (size (hnrnd (1, ones (2, 2))), [2, 2])
***** assert (size (hnrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (hnrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (hnrnd (1, 1, 3)), [3, 3])
***** assert (size (hnrnd (1, 1, [4 1])), [4, 1])
***** assert (size (hnrnd (1, 1, 4, 1)), [4, 1])
***** test
 r =  hnrnd (1, [1, 0, -1]);
 assert (r([2:3]), [NaN, NaN])
***** assert (class (hnrnd (1, 0)), "double")
***** assert (class (hnrnd (1, single (0))), "single")
***** assert (class (hnrnd (1, single ([0 0]))), "single")
***** assert (class (hnrnd (1, single (1))), "single")
***** assert (class (hnrnd (1, single ([1 1]))), "single")
***** assert (class (hnrnd (single (1), 1)), "single")
***** assert (class (hnrnd (single ([1 1]), 1)), "single")
***** error<hnrnd: function called with too few input arguments.> hnrnd ()
***** error<hnrnd: function called with too few input arguments.> hnrnd (1)
***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
 hnrnd (ones (3), ones (2))
***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
 hnrnd (ones (2), ones (3))
***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (i, 2, 3)
***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (1, i, 3)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, -1)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, 1.2)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, ones (2))
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, [2 -1 2])
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, [2 0 2.5])
***** error<hnrnd: dimensions must be non-negative integers.> ...
 hnrnd (1, 2, 2, -1, 5)
***** error<hnrnd: dimensions must be non-negative integers.> ...
 hnrnd (1, 2, 2, 1.5, 5)
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), 3)
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), [3, 2])
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/trnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/trnd.m
***** assert (size (trnd (2)), [1, 1])
***** assert (size (trnd (ones (2,1))), [2, 1])
***** assert (size (trnd (ones (2,2))), [2, 2])
***** assert (size (trnd (1, 3)), [3, 3])
***** assert (size (trnd (1, [4 1])), [4, 1])
***** assert (size (trnd (1, 4, 1)), [4, 1])
***** assert (size (trnd (1, 4, 1)), [4, 1])
***** assert (size (trnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (trnd (1, 0, 1)), [0, 1])
***** assert (size (trnd (1, 1, 0)), [1, 0])
***** assert (size (trnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (trnd (0, 1, 1), NaN)
***** assert (trnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (trnd (2)), "double")
***** assert (class (trnd (single (2))), "single")
***** assert (class (trnd (single ([2 2]))), "single")
***** error<trnd: function called with too few input arguments.> trnd ()
***** error<trnd: DF must not be complex.> trnd (i)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, -1)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, 1.2)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, ones (2))
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, [2 -1 2])
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, [2 0 2.5])
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (ones (2), ones (2))
***** error<trnd: dimensions must be non-negative integers.> ...
 trnd (1, 2, -1, 5)
***** error<trnd: dimensions must be non-negative integers.> ...
 trnd (1, 2, 1.5, 5)
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 3)
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), [3, 2])
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/fpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/fpdf.m
***** demo
 ## Plot various PDFs from the F distribution
 x = 0.01:0.01:4;
 y1 = fpdf (x, 1, 1);
 y2 = fpdf (x, 2, 1);
 y3 = fpdf (x, 5, 2);
 y4 = fpdf (x, 10, 1);
 y5 = fpdf (x, 100, 100);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 2.5])
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "northeast")
 title ("F PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = [0 0 4/9 1/4 1/9];
***** assert (fpdf (x, 2*ones (1,5), 2*ones (1,5)), y, eps)
***** assert (fpdf (x, 2, 2*ones (1,5)), y, eps)
***** assert (fpdf (x, 2*ones (1,5), 2), y, eps)
***** assert (fpdf (x, [0 NaN Inf 2 2], 2), [NaN NaN NaN y(4:5)], eps)
***** assert (fpdf (x, 2, [0 NaN Inf 2 2]), [NaN NaN NaN y(4:5)], eps)
***** assert (fpdf ([x, NaN], 2, 2), [y, NaN], eps)
***** test #F (x, 1, df1) == T distribution (sqrt (x), df1) / sqrt (x)
 rand ("seed", 1234);    # for reproducibility
 xr = rand (10,1);
 xr = xr(x > 0.1 & x < 0.9);
 yr = tpdf (sqrt (xr), 2) ./ sqrt (xr);
 assert (fpdf (xr, 1, 2), yr, 5*eps);
***** assert (fpdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
***** assert (fpdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
***** assert (fpdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
***** error<fpdf: function called with too few input arguments.> fpdf ()
***** error<fpdf: function called with too few input arguments.> fpdf (1)
***** error<fpdf: function called with too few input arguments.> fpdf (1,2)
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (3), ones (2), ones (2))
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (2), ones (3), ones (2))
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (2), ones (2), ones (3))
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (i, 2, 2)
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, i, 2)
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchyrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/cauchyrnd.m
***** assert (size (cauchyrnd (1, 1)), [1 1])
***** assert (size (cauchyrnd (1, ones (2,1))), [2, 1])
***** assert (size (cauchyrnd (1, ones (2,2))), [2, 2])
***** assert (size (cauchyrnd (ones (2,1), 1)), [2, 1])
***** assert (size (cauchyrnd (ones (2,2), 1)), [2, 2])
***** assert (size (cauchyrnd (1, 1, 3)), [3, 3])
***** assert (size (cauchyrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (cauchyrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (cauchyrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (cauchyrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (cauchyrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (cauchyrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (cauchyrnd (1, 1)), "double")
***** assert (class (cauchyrnd (1, single (1))), "single")
***** assert (class (cauchyrnd (1, single ([1, 1]))), "single")
***** assert (class (cauchyrnd (single (1), 1)), "single")
***** assert (class (cauchyrnd (single ([1, 1]), 1)), "single")
***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd ()
***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd (1)
***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
 cauchyrnd (ones (3), ones (2))
***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
 cauchyrnd (ones (2), ones (3))
***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (i, 2, 3)
***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (1, i, 3)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, -1)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, 1.2)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, ones (2))
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, [2 -1 2])
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, [2 0 2.5])
***** error<cauchyrnd: dimensions must be non-negative integers.> ...
 cauchyrnd (1, 2, 2, -1, 5)
***** error<cauchyrnd: dimensions must be non-negative integers.> ...
 cauchyrnd (1, 2, 2, 1.5, 5)
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), 3)
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), [3, 2])
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gumbelinv.m
***** demo
 ## Plot various iCDFs from the Gumbel distribution
 p = 0.001:0.001:0.999;
 x1 = gumbelinv (p, 0.5, 2);
 x2 = gumbelinv (p, 1.0, 2);
 x3 = gumbelinv (p, 1.5, 3);
 x4 = gumbelinv (p, 3.0, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 20])
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northwest")
 title ("Gumbel iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [0, 0.05, 0.5 0.95];
 x = [-Inf, -1.0972, 0.3665, 2.9702];
***** assert (gumbelinv (p), x, 1e-4)
***** assert (gumbelinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
***** assert (gumbelinv (p, 0, ones (1,4)), x, 1e-4)
***** assert (gumbelinv (p, zeros (1,4), 1), x, 1e-4)
***** assert (gumbelinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
***** assert (gumbelinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
***** assert (gumbelinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
***** assert (gumbelinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
***** assert (gumbelinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
***** assert (gumbelinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
***** assert (gumbelinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
 p = [0.05, 0.5, 0.95];
 x = gumbelinv(p);
***** assert (gumbelcdf(x), p, 1e-4)
***** error<gumbelinv: invalid number of input arguments.> gumbelinv ()
***** error gumbelinv (1,2,3,4,5,6)
***** error<gumbelinv: P, MU, and BETA must be of common size or scalars.> ...
 gumbelinv (ones (3), ones (2), ones (2))
***** error<gumbelinv: invalid size of covariance matrix.> ...
 [p, plo, pup] = gumbelinv (2, 3, 4, [1, 2])
***** error<gumbelinv: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gumbelinv (1, 2, 3)
***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
 gumbelinv (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
 gumbelinv (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (i, 2, 2)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, i, 2)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, 2, i)
***** error<gumbelinv: bad covariance matrix.> ...
 [p, plo, pup] = gumbelinv (1, 2, 3, [-1, 10; -Inf, -Inf], 0.04)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/exppdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/exppdf.m
***** demo
 ## Plot various PDFs from the exponential distribution
 x = 0:0.01:5;
 y1 = exppdf (x, 2/3);
 y2 = exppdf (x, 1.0);
 y3 = exppdf (x, 2.0);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
 grid on
 ylim ([0, 1.5])
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northeast")
 title ("Exponential PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x,y
 x = [-1 0 0.5 1 Inf];
 y = gampdf (x, 1, 2);
***** assert (exppdf (x, 2*ones (1,5)), y)
***** assert (exppdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
***** assert (exppdf ([x, NaN], 2), [y, NaN])
***** assert (exppdf (single ([x, NaN]), 2), single ([y, NaN]))
***** assert (exppdf ([x, NaN], single (2)), single ([y, NaN]))
***** error<exppdf: function called with too few input arguments.> exppdf ()
***** error<exppdf: function called with too many inputs> exppdf (1,2,3)
***** error<exppdf: X and MU must be of common size or scalars.> ...
 exppdf (ones (3), ones (2))
***** error<exppdf: X and MU must be of common size or scalars.> ...
 exppdf (ones (2), ones (3))
***** error<exppdf: X and MU must not be complex.> exppdf (i, 2)
***** error<exppdf: X and MU must not be complex.> exppdf (2, i)
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nctinv.m
***** demo
 ## Plot various iCDFs from the noncentral T distribution
 p = 0.001:0.001:0.999;
 x1 = nctinv (p, 1, 0);
 x2 = nctinv (p, 4, 0);
 x3 = nctinv (p, 1, 2);
 x4 = nctinv (p, 4, 2);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([-5, 5])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northwest")
 title ("Noncentral T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral T iCDF with MU = 1 to the T iCDF
 ## with the same number of degrees of freedom (10).

 p = 0.001:0.001:0.999;
 x1 = nctinv (p, 10, 1);
 x2 = tinv (p, 10);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([-5, 5])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
 title ("Noncentral T vs T quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [-Inf,-0.3347,0.1756,0.5209,0.8279,1.1424,1.5021,1.9633,2.6571,4.0845,Inf];
 assert (nctinv ([0:0.1:1], 2, 1), x, 1e-4);
***** test
 x = [-Inf,1.5756,2.0827,2.5343,3.0043,3.5406,4.2050,5.1128,6.5510,9.6442,Inf];
 assert (nctinv ([0:0.1:1], 2, 3), x, 1e-4);
***** test
 x = [-Inf,2.2167,2.9567,3.7276,4.6464,5.8455,7.5619,10.3327,15.7569,31.8159,Inf];
 assert (nctinv ([0:0.1:1], 1, 4), x, 1e-4);
***** test
 x = [1.7791   1.9368   2.0239   2.0801   2.1195   2.1489];
 assert (nctinv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
***** test
 x = [-0.7755, 0.3670, 1.2554, 2.0239, 2.7348, 3.4154];
 assert (nctinv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
***** test
 x = [-0.7183, 0.3624, 1.2878, 2.1195, -3.5413, 3.6430];
 assert (nctinv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
***** test
 assert (nctinv (0.996, 5, 8), 30.02610554063658, 2e-11);
***** error<nctinv: function called with too few input arguments.> nctinv ()
***** error<nctinv: function called with too few input arguments.> nctinv (1)
***** error<nctinv: function called with too few input arguments.> nctinv (1, 2)
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (3), ones (2), ones (2))
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (2), ones (3), ones (2))
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (2), ones (2), ones (3))
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (i, 2, 2)
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, i, 2)
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/tcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tcdf.m
***** demo
 ## Plot various CDFs from the Student's T distribution
 x = -5:0.01:5;
 p1 = tcdf (x, 1);
 p2 = tcdf (x, 2);
 p3 = tcdf (x, 5);
 p4 = tcdf (x, Inf);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
 grid on
 xlim ([-5, 5])
 ylim ([0, 1])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "southeast")
 title ("Student's T CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x,y
 x = [-Inf 0 1 Inf];
 y = [0 1/2 3/4 1];
***** assert (tcdf (x, ones (1,4)), y, eps)
***** assert (tcdf (x, 1), y, eps)
***** assert (tcdf (x, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
***** assert (tcdf ([x(1:2) NaN x(4)], 1), [y(1:2) NaN y(4)], eps)
***** assert (tcdf (2, 3, "upper"), 0.0697, 1e-4)
***** assert (tcdf (205, 5, "upper"), 2.6206e-11, 1e-14)
***** assert (tcdf ([x, NaN], 1), [y, NaN], eps)
***** assert (tcdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (tcdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<tcdf: function called with too few input arguments.> tcdf ()
***** error<tcdf: function called with too few input arguments.> tcdf (1)
***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, "uper")
***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, 3)
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2))
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2))
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2), "upper")
***** error<tcdf: X and DF must not be complex.> tcdf (i, 2)
***** error<tcdf: X and DF must not be complex.> tcdf (2, i)
***** shared tol_rel
 tol_rel = 10 * eps;
***** assert (tcdf (10^(-10), 2.5), 0.50000000003618087, -tol_rel)
***** assert (tcdf (10^(-11), 2.5), 0.50000000000361809, -tol_rel)
***** assert (tcdf (10^(-12), 2.5), 0.50000000000036181, -tol_rel)
***** assert (tcdf (10^(-13), 2.5), 0.50000000000003618, -tol_rel)
***** assert (tcdf (10^(-14), 2.5), 0.50000000000000362, -tol_rel)
***** assert (tcdf (10^(-15), 2.5), 0.50000000000000036, -tol_rel)
***** assert (tcdf (10^(-16), 2.5), 0.50000000000000004, -tol_rel)
***** assert (tcdf (-10^1, 2.5), 2.2207478836537124e-03, -tol_rel)
***** assert (tcdf (-10^2, 2.5), 7.1916492116661878e-06, -tol_rel)
***** assert (tcdf (-10^3, 2.5), 2.2747463948307452e-08, -tol_rel)
***** assert (tcdf (-10^4, 2.5), 7.1933970159922115e-11, -tol_rel)
***** assert (tcdf (-10^5, 2.5), 2.2747519231756221e-13, -tol_rel)
30 tests, 30 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncfpdf.m
***** demo
 ## Plot various PDFs from the noncentral F distribution
 x = 0:0.01:5;
 y1 = ncfpdf (x, 2, 5, 1);
 y2 = ncfpdf (x, 2, 5, 2);
 y3 = ncfpdf (x, 5, 10, 1);
 y4 = ncfpdf (x, 10, 20, 10);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
 grid on
 xlim ([0, 5])
 ylim ([0, 0.8])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "northeast")
 title ("Noncentral F PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral F PDF with LAMBDA = 10 to the F PDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 x = 0.01:0.1:10.01;
 y1 = ncfpdf (x, 5, 20, 10);
 y2 = fpdf (x, 5, 20);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([0, 10])
 ylim ([0, 0.8])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northeast")
 title ("Noncentral F vs F PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df1, df2, lambda
 x1 = [-Inf, 2, NaN, 4, Inf];
 df1 = [2, 0, -1, 1, 4];
 df2 = [2, 4, 5, 6, 8];
 lambda = [1, NaN, 3, -1, 2];
***** assert (ncfpdf (x1, df1, df2, lambda), [0, NaN, NaN, NaN, NaN]);
***** assert (ncfpdf (x1, df1, df2, 1), [0, NaN, NaN, ...
                                   0.05607937264237208, NaN], 1e-14);
***** assert (ncfpdf (x1, df1, df2, 3), [0, NaN, NaN, ...
                                   0.080125760971946518, NaN], 1e-14);
***** assert (ncfpdf (x1, df1, df2, 2), [0, NaN, NaN, ...
                                   0.0715902008258656, NaN], 1e-14);
***** assert (ncfpdf (x1, 3, 5, lambda), [0, NaN, NaN, NaN, NaN]);
***** assert (ncfpdf (2, df1, df2, lambda), [0.1254046999837947, NaN, NaN, ...
                                      NaN, 0.2152571783045893], 1e-14);
***** assert (ncfpdf (4, df1, df2, lambda), [0.05067089541001374, NaN, NaN, ...
                                      NaN, 0.05560846335398539], 1e-14);
***** error<ncfpdf: function called with too few input arguments.> ncfpdf ()
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1)
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2)
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2, 3)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (3), ones (2), ones (2), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (3), ones (2), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (2), ones (3), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (2), ones (2), ones (3))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (i, 2, 2, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, i, 2, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, i, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/geocdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/geocdf.m
***** demo
 ## Plot various CDFs from the geometric distribution
 x = 0:10;
 p1 = geocdf (x, 0.2);
 p2 = geocdf (x, 0.5);
 p3 = geocdf (x, 0.7);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 xlim ([0, 10])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "southeast")
 title ("Geometric CDF")
 xlabel ("values in x (number of failures)")
 ylabel ("probability")
***** test
 p = geocdf ([1, 2, 3, 4], 0.25);
 assert (p(1), 0.4375000000, 1e-14);
 assert (p(2), 0.5781250000, 1e-14);
 assert (p(3), 0.6835937500, 1e-14);
 assert (p(4), 0.7626953125, 1e-14);
***** test
 p = geocdf ([1, 2, 3, 4], 0.25, "upper");
 assert (p(1), 0.5625000000, 1e-14);
 assert (p(2), 0.4218750000, 1e-14);
 assert (p(3), 0.3164062500, 1e-14);
 assert (p(4), 0.2373046875, 1e-14);
***** shared x, p
 x = [-1 0 1 Inf];
 p = [0 0.5 0.75 1];
***** assert (geocdf (x, 0.5*ones (1,4)), p)
***** assert (geocdf (x, 0.5), p)
***** assert (geocdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN p(4)])
***** assert (geocdf ([x(1:2) NaN x(4)], 0.5), [p(1:2) NaN p(4)])
***** assert (geocdf ([x, NaN], 0.5), [p, NaN])
***** assert (geocdf (single ([x, NaN]), 0.5), single ([p, NaN]))
***** assert (geocdf ([x, NaN], single (0.5)), single ([p, NaN]))
***** error<geocdf: function called with too few input arguments.> geocdf ()
***** error<geocdf: function called with too few input arguments.> geocdf (1)
***** error<geocdf: X and PS must be of common size or scalars.> ...
 geocdf (ones (3), ones (2))
***** error<geocdf: X and PS must be of common size or scalars.> ...
 geocdf (ones (2), ones (3))
***** error<geocdf: X and PS must not be complex.> geocdf (i, 2)
***** error<geocdf: X and PS must not be complex.> geocdf (2, i)
***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, "tail")
***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, 5)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/copulapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/copulapdf.m
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 y = copulapdf ("Clayton", x, theta);
 expected_p = [0.9872; 0.7295];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 y = copulapdf ("Gumbel", x, 2);
 expected_p = [0.9468; 0.9468];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2, 0.6; 0.2, 0.6];
 theta = [1; 2];
 y = copulapdf ("Frank", x, theta);
 expected_p = [0.9378; 0.8678];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2, 0.6; 0.2, 0.6];
 theta = [0.3; 0.7];
 y = copulapdf ("AMH", x, theta);
 expected_p = [0.9540; 0.8577];
 assert (y, expected_p, 0.001);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unidinv.m
***** demo
 ## Plot various iCDFs from the discrete uniform distribution
 p = 0.001:0.001:0.999;
 x1 = unidinv (p, 5);
 x2 = unidinv (p, 9);
 plot (p, x1, "-b", p, x2, "-g")
 grid on
 xlim ([0, 1])
 ylim ([0, 10])
 legend ({"N = 5", "N = 9"}, "location", "northwest")
 title ("Discrete uniform iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (unidinv (p, 10*ones (1,5)), [NaN NaN 5 10 NaN], eps)
***** assert (unidinv (p, 10), [NaN NaN 5 10 NaN], eps)
***** assert (unidinv (p, 10*[0 1 NaN 1 1]), [NaN NaN NaN 10 NaN], eps)
***** assert (unidinv ([p(1:2) NaN p(4:5)], 10), [NaN NaN NaN 10 NaN], eps)
***** assert (unidinv ([p, NaN], 10), [NaN NaN 5 10 NaN NaN], eps)
***** assert (unidinv (single ([p, NaN]), 10), single ([NaN NaN 5 10 NaN NaN]), eps)
***** assert (unidinv ([p, NaN], single (10)), single ([NaN NaN 5 10 NaN NaN]), eps)
***** error<unidinv: function called with too few input arguments.> unidinv ()
***** error<unidinv: function called with too few input arguments.> unidinv (1)
***** error<unidinv: P and N must be of common size or scalars.> ...
 unidinv (ones (3), ones (2))
***** error<unidinv: P and N must be of common size or scalars.> ...
 unidinv (ones (2), ones (3))
***** error<unidinv: P and N must not be complex.> unidinv (i, 2)
***** error<unidinv: P and N must not be complex.> unidinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncfinv.m
***** demo
 ## Plot various iCDFs from the noncentral F distribution
 p = 0.001:0.001:0.999;
 x1 = ncfinv (p, 2, 5, 1);
 x2 = ncfinv (p, 2, 5, 2);
 x3 = ncfinv (p, 5, 10, 1);
 x4 = ncfinv (p, 10, 20, 10);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([0, 5])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "northwest")
 title ("Noncentral F iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral F iCDF with LAMBDA = 10 to the F iCDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 p = 0.001:0.001:0.999;
 x1 = ncfinv (p, 5, 20, 10);
 x2 = finv (p, 5, 20);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([0, 10])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northwest")
 title ("Noncentral F vs F quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [0,0.1775,0.3864,0.6395,0.9564,1.3712,1.9471,2.8215,4.3679,8.1865,Inf];
 assert (ncfinv ([0:0.1:1], 2, 3, 1), x, 1e-4);
***** test
 x = [0,0.7492,1.3539,2.0025,2.7658,3.7278,5.0324,6.9826,10.3955,18.7665,Inf];
 assert (ncfinv ([0:0.1:1], 2, 3, 5), x, 1e-4);
***** test
 x = [0,0.2890,0.8632,1.5653,2.4088,3.4594,4.8442,6.8286,10.0983,17.3736,Inf];
 assert (ncfinv ([0:0.1:1], 1, 4, 3), x, 1e-4);
***** test
 x = [0.078410, 0.212716, 0.288618, 0.335752, 0.367963, 0.391460];
 assert (ncfinv (0.05, [1, 2, 3, 4, 5, 6], 10, 3), x, 1e-6);
***** test
 x = [0.2574, 0.2966, 0.3188, 0.3331, 0.3432, 0.3507];
 assert (ncfinv (0.05, 5, [1, 2, 3, 4, 5, 6], 3), x, 1e-4);
***** test
 x = [1.6090, 1.8113, 1.9215, 1.9911, NaN, 2.0742];
 assert (ncfinv (0.05, 1, [1, 2, 3, 4, -1, 6], 10), x, 1e-4);
***** test
 assert (ncfinv (0.996, 3, 5, 8), 58.0912074080671, 2e-13);
***** error<ncfinv: function called with too few input arguments.> ncfinv ()
***** error<ncfinv: function called with too few input arguments.> ncfinv (1)
***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2)
***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2, 3)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (3), ones (2), ones (2), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (3), ones (2), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (2), ones (3), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (2), ones (2), ones (3))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (i, 2, 2, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, i, 2, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, i, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/plcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/plcdf.m
***** demo
 ## Plot various CDFs from the Piecewise linear distribution
 data = 0:0.01:10;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 p1 = plcdf (data, x1, Fx1);
 p2 = plcdf (data, x2, Fx2);
 plot (data, p1, "-b", data, p2, "g")
 grid on
 ylim ([0, 1])
 xlim ([0, 10])
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "southeast")
 title ("Piecewise linear CDF")
 xlabel ("values in data")
 ylabel ("probability")
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 1]);
 assert (p, data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 2], [0, 1]);
 assert (p, 0.5 * data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 0.5]);
 assert (p, 0.5 * data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 0.5], [0, 1]);
 assert (p, [0, 0.4, 0.8, 1, 1, 1]);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 1], "upper");
 assert (p, 1 - data);
***** error<plcdf: function called with too few input arguments.> plcdf ()
***** error<plcdf: function called with too few input arguments.> plcdf (1)
***** error<plcdf: function called with too few input arguments.> plcdf (1, 2)
***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, "uper")
***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, 4)
***** error<plcdf: X and FX must be vectors of equal size.> ...
 plcdf (1, [0, 1, 2], [0, 1])
***** error<plcdf: X and FX must be at least two-elements long.> ...
 plcdf (1, [0], [1])
***** error<plcdf: FX must be bounded in the range> ...
 plcdf (1, [0, 1, 2], [0, 1, 1.5])
***** error<plcdf: FX must be bounded in the range> ...
 plcdf (1, [0, 1, 2], [0, i, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (i, [0, 1, 2], [0, 0.5, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (1, [0, i, 2], [0, 0.5, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (1, [0, 1, 2], [0, 0.5i, 1])
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlscdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tlscdf.m
***** demo
 ## Plot various CDFs from the location-scale Student's T distribution
 x = -8:0.01:8;
 p1 = tlscdf (x, 0, 1, 1);
 p2 = tlscdf (x, 0, 2, 2);
 p3 = tlscdf (x, 3, 2, 5);
 p4 = tlscdf (x, -1, 3, Inf);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
 grid on
 xlim ([-8, 8])
 ylim ([0, 1])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "northwest")
 title ("Location-scale Student's T CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x,y
 x = [-Inf 0 1 Inf];
 y = [0 1/2 3/4 1];
***** assert (tlscdf (x, 0, 1, ones (1,4)), y, eps)
***** assert (tlscdf (x, 0, 1, 1), y, eps)
***** assert (tlscdf (x, 0, 1, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
***** assert (tlscdf ([x(1:2) NaN x(4)], 0, 1, 1), [y(1:2) NaN y(4)], eps)
***** assert (tlscdf (2, 0, 1, 3, "upper"), 0.0697, 1e-4)
***** assert (tlscdf (205, 0, 1, 5, "upper"), 2.6206e-11, 1e-14)
***** assert (tlscdf ([x, NaN], 0, 1, 1), [y, NaN], eps)
***** assert (tlscdf (single ([x, NaN]), 0, 1, 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], single (0), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], 0, single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], 0, 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<tlscdf: function called with too few input arguments.> tlscdf ()
***** error<tlscdf: function called with too few input arguments.> tlscdf (1)
***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2)
***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2, 3)
***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, "uper")
***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, 5)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), ones (2), 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, ones (2), 1)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, 1, ones (2))
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), ones (2), 1, 1, "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, ones (2), 1, "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, 1, ones (2), "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (i, 2, 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, i, 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, i, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, 1, i)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/geopdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/geopdf.m
***** demo
 ## Plot various PDFs from the geometric distribution
 x = 0:10;
 y1 = geopdf (x, 0.2);
 y2 = geopdf (x, 0.5);
 y3 = geopdf (x, 0.7);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.8])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northeast")
 title ("Geometric PDF")
 xlabel ("values in x (number of failures)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 Inf];
 y = [0, 1/2, 1/4, NaN];
***** assert (geopdf (x, 0.5*ones (1,4)), y)
***** assert (geopdf (x, 0.5), y)
***** assert (geopdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN y(4)])
***** assert (geopdf ([x, NaN], 0.5), [y, NaN])
***** assert (geopdf (single ([x, NaN]), 0.5), single ([y, NaN]), 5*eps ("single"))
***** assert (geopdf ([x, NaN], single (0.5)), single ([y, NaN]), 5*eps ("single"))
***** error geopdf ()
***** error geopdf (1)
***** error geopdf (1,2,3)
***** error geopdf (ones (3), ones (2))
***** error geopdf (ones (2), ones (3))
***** error geopdf (i, 2)
***** error geopdf (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/evcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/evcdf.m
***** demo
 ## Plot various CDFs from the extreme value distribution
 x = -10:0.01:10;
 p1 = evcdf (x, 0.5, 2);
 p2 = evcdf (x, 1.0, 2);
 p3 = evcdf (x, 1.5, 3);
 p4 = evcdf (x, 3.0, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "southeast")
 title ("Extreme value CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = [0, 0.6321, 0.9340, 1];
***** assert (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
***** assert (evcdf (x, 1, ones (1,4)), y, 1e-4)
***** assert (evcdf (x, ones (1,4), 1), y, 1e-4)
***** assert (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
***** assert (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
***** assert (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
***** assert (evcdf (x, "upper"), [1, 0.0660, 0.0006, 0], 1e-4)
***** assert (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
***** assert (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
***** assert (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
***** error<evcdf: invalid number of input arguments.> evcdf ()
***** error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
***** error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, "uper")
***** error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 evcdf (ones (3), ones (2), ones (2))
***** error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
***** error<evcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = evcdf (1, 2, 3)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
***** error<evcdf: bad covariance matrix.> ...
 [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gevcdf.m
***** demo
 ## Plot various CDFs from the generalized extreme value distribution
 x = -1:0.001:10;
 p1 = gevcdf (x, 1, 1, 1);
 p2 = gevcdf (x, 0.5, 1, 1);
 p3 = gevcdf (x, 1, 1, 5);
 p4 = gevcdf (x, 1, 2, 5);
 p5 = gevcdf (x, 1, 5, 5);
 p6 = gevcdf (x, 1, 0.5, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 xlim ([-1, 10])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "southeast")
 title ("Generalized extreme value CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 k = 1;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0.36788, 0.44933, 0.47237, 0.48323, 0.48954, 0.49367];
 assert (p, expected_p, 0.001);
***** test
 x = -0.5:0.5:2.5;
 sigma = 0.5;
 k = 1;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0, 0.36788, 0.60653, 0.71653, 0.77880, 0.81873, 0.84648];
 assert (p, expected_p, 0.001);
***** test # check for continuity for k near 0
 x = 1;
 sigma = 0.5;
 k = -0.03:0.01:0.03;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0.88062, 0.87820, 0.87580, 0.87342, 0.87107, 0.86874, 0.86643];
 assert (p, expected_p, 0.001);
***** error<gevcdf: function called with too few input arguments.> gevcdf ()
***** error<gevcdf: function called with too few input arguments.> gevcdf (1)
***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2)
***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2, 3)
***** error<gevcdf: function called with too many inputs> ...
 gevcdf (1, 2, 3, 4, 5, 6)
***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, "tail")
***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, 5)
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (i, 2, 3, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, i, 3, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, i, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, 3, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/loglpdf.m
***** demo
 ## Plot various PDFs from the log-logistic distribution
 x = 0.001:0.001:2;
 y1 = loglpdf (x, log (1), 1/0.5);
 y2 = loglpdf (x, log (1), 1);
 y3 = loglpdf (x, log (1), 1/2);
 y4 = loglpdf (x, log (1), 1/4);
 y5 = loglpdf (x, log (1), 1/8);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0,3])
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northeast")
 title ("Log-logistic PDF")
 xlabel ("values in x")
 ylabel ("density")
 text (0.1, 2.8, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared out1, out2
 out1 = [0, 0, 1, 0.2500, 0.1111, 0.0625, 0.0400, 0.0278, 0];
 out2 = [0, 0, 0.0811, 0.0416, 0.0278, 0.0207, 0.0165, 0];
***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
***** assert (loglpdf ([-1:5,Inf], 1, 3), out2, 1e-4)
***** assert (class (loglpdf (single (1), 2, 3)), "single")
***** assert (class (loglpdf (1, single (2), 3)), "single")
***** assert (class (loglpdf (1, 2, single (3))), "single")
***** error<loglpdf: function called with too few input arguments.> loglpdf (1)
***** error<loglpdf: function called with too few input arguments.> loglpdf (1, 2)
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (1, ones (2), ones (3))
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (ones (2), 1, ones (3))
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (ones (2), ones (3), 1)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (i, 2, 3)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, i, 3)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/vmpdf.m
***** demo
 ## Plot various PDFs from the von Mises distribution
 x1 = [-pi:0.1:pi];
 y1 = vmpdf (x1, 0, 0.5);
 y2 = vmpdf (x1, 0, 1);
 y3 = vmpdf (x1, 0, 2);
 y4 = vmpdf (x1, 0, 4);
 plot (x1, y1, "-r", x1, y2, "-g", x1, y3, "-b", x1, y4, "-c")
 grid on
 xlim ([-pi, pi])
 ylim ([0, 0.8])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-pi:pi/2:pi];
 y0 = [0.046245, 0.125708, 0.341710, 0.125708, 0.046245];
 y1 = [0.046245, 0.069817, 0.654958, 0.014082, 0.000039];
***** assert (vmpdf (x, 0, 1), y0, 1e-5)
***** assert (vmpdf (x, zeros (1,5), ones (1,5)), y0, 1e-6)
***** assert (vmpdf (x, 0, [1 2 3 4 5]), y1, 1e-6)
***** assert (isa (vmpdf (single (pi), 0, 1), "single"), true)
***** assert (isa (vmpdf (pi, single (0), 1), "single"), true)
***** assert (isa (vmpdf (pi, 0, single (1)), "single"), true)
***** error<vmpdf: function called with too few input arguments.> vmpdf ()
***** error<vmpdf: function called with too few input arguments.> vmpdf (1)
***** error<vmpdf: function called with too few input arguments.> vmpdf (1, 2)
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (3), ones (2), ones (2))
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (2), ones (3), ones (2))
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (2), ones (2), ones (3))
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (i, 2, 2)
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, i, 2)
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/betapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/betapdf.m
***** demo
 ## Plot various PDFs from the Beta distribution
 x = 0.001:0.001:0.999;
 y1 = betapdf (x, 0.5, 0.5);
 y2 = betapdf (x, 5, 1);
 y3 = betapdf (x, 1, 3);
 y4 = betapdf (x, 2, 2);
 y5 = betapdf (x, 2, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 2.5])
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "north")
 title ("Beta PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = [0 2 1 0 0];
***** assert (betapdf (x, ones (1, 5), 2 * ones (1, 5)), y)
***** assert (betapdf (x, 1, 2 * ones (1, 5)), y)
***** assert (betapdf (x, ones (1, 5), 2), y)
***** assert (betapdf (x, [0 NaN 1 1 1], 2), [NaN NaN y(3:5)])
***** assert (betapdf (x, 1, 2 * [0 NaN 1 1 1]), [NaN NaN y(3:5)])
***** assert (betapdf ([x, NaN], 1, 2), [y, NaN])
***** assert (betapdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (betapdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (betapdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** test
 x = rand (10,1);
 y = 1 ./ (pi * sqrt (x .* (1 - x)));
 assert (betapdf (x, 1/2, 1/2), y, 1e-12);
***** assert (betapdf (0.5, 1000, 1000), 35.678, 1e-3)
***** error<betapdf: function called with too few input arguments.> betapdf ()
***** error<betapdf: function called with too few input arguments.> betapdf (1)
***** error<betapdf: function called with too few input arguments.> betapdf (1,2)
***** error<betapdf: function called with too many inputs> betapdf (1,2,3,4)
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (3), ones (2), ones (2))
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (2), ones (3), ones (2))
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (2), ones (2), ones (3))
***** error<betapdf: X, A, and B must not be complex.> betapdf (i, 2, 2)
***** error<betapdf: X, A, and B must not be complex.> betapdf (2, i, 2)
***** error<betapdf: X, A, and B must not be complex.> betapdf (2, 2, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/laplacepdf.m
***** demo
 ## Plot various PDFs from the Laplace distribution
 x = -10:0.01:10;
 y1 = laplacepdf (x, 0, 1);
 y2 = laplacepdf (x, 0, 2);
 y3 = laplacepdf (x, 0, 4);
 y4 = laplacepdf (x, -5, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-10, 10])
 ylim ([0, 0.6])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northeast")
 title ("Laplace PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf -log(2) 0 log(2) Inf];
 y = [0, 1/4, 1/2, 1/4, 0];
***** assert (laplacepdf ([x, NaN], 0, 1), [y, NaN])
***** assert (laplacepdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.25, 0])
***** assert (laplacepdf (single ([x, NaN]), 0, 1), single ([y, NaN]))
***** assert (laplacepdf ([x, NaN], single (0), 1), single ([y, NaN]))
***** assert (laplacepdf ([x, NaN], 0, single (1)), single ([y, NaN]))
***** error<laplacepdf: function called with too few input arguments.> laplacepdf ()
***** error<laplacepdf: function called with too few input arguments.> laplacepdf (1)
***** error<laplacepdf: function called with too few input arguments.> ...
 laplacepdf (1, 2)
***** error<laplacepdf: function called with too many inputs> laplacepdf (1, 2, 3, 4)
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (1, ones (2), ones (3))
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (ones (2), 1, ones (3))
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (ones (2), ones (3), 1)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (i, 2, 3)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, i, 3)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/logipdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logipdf.m
***** demo
 ## Plot various PDFs from the logistic distribution
 x = -5:0.01:20;
 y1 = logipdf (x, 5, 2);
 y2 = logipdf (x, 9, 3);
 y3 = logipdf (x, 9, 4);
 y4 = logipdf (x, 6, 2);
 y5 = logipdf (x, 2, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 0.3])
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "northeast")
 title ("Logistic PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf -log(4) 0 log(4) Inf];
 y = [0, 0.16, 1/4, 0.16, 0];
***** assert (logipdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (logipdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), y([4:5])], eps)
***** assert (logipdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (logipdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (logipdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<logipdf: function called with too few input arguments.> logipdf ()
***** error<logipdf: function called with too few input arguments.> logipdf (1)
***** error<logipdf: function called with too few input arguments.> ...
 logipdf (1, 2)
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (1, ones (2), ones (3))
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (ones (2), 1, ones (3))
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (ones (2), ones (3), 1)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (i, 2, 3)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, i, 3)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nakarnd.m
***** assert (size (nakarnd (1, 1)), [1 1])
***** assert (size (nakarnd (1, ones (2,1))), [2, 1])
***** assert (size (nakarnd (1, ones (2,2))), [2, 2])
***** assert (size (nakarnd (ones (2,1), 1)), [2, 1])
***** assert (size (nakarnd (ones (2,2), 1)), [2, 2])
***** assert (size (nakarnd (1, 1, 3)), [3, 3])
***** assert (size (nakarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (nakarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (nakarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (nakarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (nakarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (nakarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (nakarnd (1, 1)), "double")
***** assert (class (nakarnd (1, single (1))), "single")
***** assert (class (nakarnd (1, single ([1, 1]))), "single")
***** assert (class (nakarnd (single (1), 1)), "single")
***** assert (class (nakarnd (single ([1, 1]), 1)), "single")
***** error<nakarnd: function called with too few input arguments.> nakarnd ()
***** error<nakarnd: function called with too few input arguments.> nakarnd (1)
***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
 nakarnd (ones (3), ones (2))
***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
 nakarnd (ones (2), ones (3))
***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (i, 2, 3)
***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (1, i, 3)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, -1)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, 1.2)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, ones (2))
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, [2 -1 2])
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, [2 0 2.5])
***** error<nakarnd: dimensions must be non-negative integers.> ...
 nakarnd (1, 2, 2, -1, 5)
***** error<nakarnd: dimensions must be non-negative integers.> ...
 nakarnd (1, 2, 2, 1.5, 5)
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), 3)
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), [3, 2])
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/logiinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logiinv.m
***** demo
 ## Plot various iCDFs from the logistic distribution
 p = 0.001:0.001:0.999;
 x1 = logiinv (p, 5, 2);
 x2 = logiinv (p, 9, 3);
 x3 = logiinv (p, 9, 4);
 x4 = logiinv (p, 6, 2);
 x5 = logiinv (p, 2, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
 title ("Logistic iCDF")
 xlabel ("probability")
 ylabel ("x")
***** test
 p = [0.01:0.01:0.99];
 assert (logiinv (p, 0, 1), log (p ./ (1-p)), 25*eps);
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (logiinv (p, 0, 1), [NaN -Inf 0 Inf NaN])
***** assert (logiinv (p, 0, [-1, 0, 1, 2, 3]), [NaN NaN 0 Inf NaN])
***** assert (logiinv ([p, NaN], 0, 1), [NaN -Inf 0 Inf NaN NaN])
***** assert (logiinv (single ([p, NaN]), 0, 1), single ([NaN -Inf 0 Inf NaN NaN]))
***** assert (logiinv ([p, NaN], single (0), 1), single ([NaN -Inf 0 Inf NaN NaN]))
***** assert (logiinv ([p, NaN], 0, single (1)), single ([NaN -Inf 0 Inf NaN NaN]))
***** error<logiinv: function called with too few input arguments.> logiinv ()
***** error<logiinv: function called with too few input arguments.> logiinv (1)
***** error<logiinv: function called with too few input arguments.> ...
 logiinv (1, 2)
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (1, ones (2), ones (3))
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (ones (2), 1, ones (3))
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (ones (2), ones (3), 1)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (i, 2, 3)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, i, 3)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/vmrnd.m
***** assert (size (vmrnd (1, 1)), [1 1])
***** assert (size (vmrnd (1, ones (2,1))), [2, 1])
***** assert (size (vmrnd (1, ones (2,2))), [2, 2])
***** assert (size (vmrnd (ones (2,1), 1)), [2, 1])
***** assert (size (vmrnd (ones (2,2), 1)), [2, 2])
***** assert (size (vmrnd (1, 1, 3)), [3, 3])
***** assert (size (vmrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (vmrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (vmrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (vmrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (vmrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (vmrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (vmrnd (1, 1)), "double")
***** assert (class (vmrnd (1, single (1))), "single")
***** assert (class (vmrnd (1, single ([1, 1]))), "single")
***** assert (class (vmrnd (single (1), 1)), "single")
***** assert (class (vmrnd (single ([1, 1]), 1)), "single")
***** error<vmrnd: function called with too few input arguments.> vmrnd ()
***** error<vmrnd: function called with too few input arguments.> vmrnd (1)
***** error<vmrnd: MU and K must be of common size or scalars.> ...
 vmrnd (ones (3), ones (2))
***** error<vmrnd: MU and K must be of common size or scalars.> ...
 vmrnd (ones (2), ones (3))
***** error<vmrnd: MU and K must not be complex.> vmrnd (i, 2, 3)
***** error<vmrnd: MU and K must not be complex.> vmrnd (1, i, 3)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, -1)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, 1.2)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, ones (2))
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, [2 -1 2])
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, [2 0 2.5])
***** error<vmrnd: dimensions must be non-negative integers.> ...
 vmrnd (1, 2, 2, -1, 5)
***** error<vmrnd: dimensions must be non-negative integers.> ...
 vmrnd (1, 2, 2, 1.5, 5)
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), 3)
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), [3, 2])
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/mnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mnpdf.m
***** test
 x = [1, 4, 2];
 pk = [0.2, 0.5, 0.3];
 y = mnpdf (x, pk);
 assert (y, 0.11812, 0.001);
***** test
 x = [1, 4, 2; 1, 0, 9];
 pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
 y = mnpdf (x, pk);
 assert (y, [0.11812; 0.13422], 0.001);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncx2cdf.m
***** demo
 ## Plot various CDFs from the noncentral chi-squared distribution
 x = 0:0.1:10;
 p1 = ncx2cdf (x, 2, 1);
 p2 = ncx2cdf (x, 2, 2);
 p3 = ncx2cdf (x, 2, 3);
 p4 = ncx2cdf (x, 4, 1);
 p5 = ncx2cdf (x, 4, 2);
 p6 = ncx2cdf (x, 4, 3);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", ...
       x, p4, "-m", x, p5, "-c", x, p6, "-y")
 grid on
 xlim ([0, 10])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "southeast")
 title ("Noncentral chi-squared CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
 ## chi-squared CDF with the same number of degrees of freedom (4).

 x = 0:0.1:10;
 p1 = ncx2cdf (x, 4, 2);
 p2 = chi2cdf (x, 4);
 plot (x, p1, "-", x, p2, "-")
 grid on
 xlim ([0, 10])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = ncx2cdf (x, 10, 1);
 assert (p([1:21]), zeros (1, 21), 3e-84);
 assert (p(22), 1.521400636466575e-09, 1e-14);
 assert (p(30), 6.665480510026046e-05, 1e-14);
 assert (p(41), 0.002406447308399836, 1e-14);
***** test
 p = ncx2cdf (12, 10, 3);
 assert (p, 0.4845555602398649, 1e-14);
***** test
 p = ncx2cdf (2, 3, 2);
 assert (p, 0.2207330870741212, 1e-14);
***** test
 p = ncx2cdf (2, 3, 2, "upper");
 assert (p, 0.7792669129258789, 1e-14);
***** test
 p = ncx2cdf ([3, 6], 3, 2, "upper");
 assert (p, [0.6423318186400054, 0.3152299878943012], 1e-14);
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf ()
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1)
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1, 2)
***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, "tail")
***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, 4)
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (3), ones (2), ones (2))
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (2), ones (3), ones (2))
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (2), ones (2), ones (3))
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (i, 2, 2)
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, i, 2)
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/binopdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/binopdf.m
***** demo
 ## Plot various PDFs from the binomial distribution
 x = 0:40;
 y1 = binopdf (x, 20, 0.5);
 y2 = binopdf (x, 20, 0.7);
 y3 = binopdf (x, 40, 0.5);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.25])
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "northeast")
 title ("Binomial PDF")
 xlabel ("values in x (number of successes)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/4 1/2 1/4 0];
***** assert (binopdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), y, eps)
***** assert (binopdf (x, 2, 0.5 * ones (1, 5)), y, eps)
***** assert (binopdf (x, 2 * ones (1, 5), 0.5), y, eps)
***** assert (binopdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 0])
***** assert (binopdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 0])
***** assert (binopdf ([x, NaN], 2, 0.5), [y, NaN], eps)
***** assert (binopdf (cat (3, x, x), 2, 0.5), cat (3, y, y), eps)
***** assert (binopdf (1, 1, 1), 1)
***** assert (binopdf (0, 3, 0), 1)
***** assert (binopdf (2, 2, 1), 1)
***** assert (binopdf (1, 2, 1), 0)
***** assert (binopdf (0, 1.1, 0), NaN)
***** assert (binopdf (1, 2, -1), NaN)
***** assert (binopdf (1, 2, 1.5), NaN)
***** assert (binopdf ([], 1, 1), [])
***** assert (binopdf (1, [], 1), [])
***** assert (binopdf (1, 1, []), [])
***** assert (binopdf (ones (1, 0), 2, .5), ones(1, 0))
***** assert (binopdf (ones (0, 1), 2, .5), ones(0, 1))
***** assert (binopdf (ones (0, 1, 2), 2, .5), ones(0, 1, 2))
***** assert (binopdf (1, ones (0, 1, 2), .5), ones(0, 1, 2))
***** assert (binopdf (1, 2, ones (0, 1, 2)), ones(0, 1, 2))
***** assert (binopdf (ones (1, 0, 2), 2, .5), ones(1, 0, 2))
***** assert (binopdf (ones (1, 2, 0), 2, .5), ones(1, 2, 0))
***** assert (binopdf (ones (0, 1, 2), NaN, .5), ones(0, 1, 2))
***** assert (binopdf (ones (0, 1, 2), 2, NaN), ones(0, 1, 2))
***** assert (binopdf (single ([x, NaN]), 2, 0.5), single ([y, NaN]))
***** assert (binopdf ([x, NaN], single (2), 0.5), single ([y, NaN]))
***** assert (binopdf ([x, NaN], 2, single (0.5)), single ([y, NaN]))
***** error<binopdf: function called with too few input arguments.> binopdf ()
***** error<binopdf: function called with too few input arguments.> binopdf (1)
***** error<binopdf: function called with too few input arguments.> binopdf (1, 2)
***** error<binopdf: function called with too many inputs> binopdf (1, 2, 3, 4)
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (3), ones (2), ones (2))
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (2), ones (3), ones (2))
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (2), ones (2), ones (3))
***** error<binopdf: X, N, and PS must not be complex.> binopdf (i, 2, 2)
***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, i, 2)
***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, 2, i)
39 tests, 39 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygeinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hygeinv.m
***** demo
 ## Plot various iCDFs from the hypergeometric distribution
 p = 0.001:0.001:0.999;
 x1 = hygeinv (p, 500, 50, 100);
 x2 = hygeinv (p, 500, 60, 200);
 x3 = hygeinv (p, 500, 70, 300);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 60])
 legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
          "m = 500, k = 70, n = 300"}, "location", "northwest")
 title ("Hypergeometric iCDF")
 xlabel ("probability")
 ylabel ("values in p (number of successes)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (hygeinv (p, 4*ones (1,5), 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4*ones (1,5), 2, 2), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4, 2*ones (1,5), 2), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4, 2, 2*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4*[1 -1 NaN 1.1 1], 2, 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2*[1 -1 NaN 1.1 1], 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 5, 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2, 2*[1 -1 NaN 1.1 1]), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2, 5), [NaN NaN NaN NaN NaN])
***** assert (hygeinv ([p(1:2) NaN p(4:5)], 4, 2, 2), [NaN 0 NaN 2 NaN])
***** assert (hygeinv ([p, NaN], 4, 2, 2), [NaN 0 1 2 NaN NaN])
***** assert (hygeinv (single ([p, NaN]), 4, 2, 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], single (4), 2, 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], 4, single (2), 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], 4, 2, single (2)), single ([NaN 0 1 2 NaN NaN]))
***** error<hygeinv: function called with too few input arguments.> hygeinv ()
***** error<hygeinv: function called with too few input arguments.> hygeinv (1)
***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2)
***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2,3)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (ones (2), ones (3), 1, 1)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (1, ones (2), ones (3), 1)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (1, 1, ones (2), ones (3))
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (i, 2, 2, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, i, 2, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, i, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, 2, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/logncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logncdf.m
***** demo
 ## Plot various CDFs from the log-normal distribution
 x = 0:0.01:3;
 p1 = logncdf (x, 0, 1);
 p2 = logncdf (x, 0, 0.5);
 p3 = logncdf (x, 0, 0.25);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
 grid on
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
         "location", "southeast")
 title ("Log-normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, e, Inf];
 y = [0, 0, 0.5, 1/2+1/2*erf(1/2), 1];
***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5)), y, eps)
***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5), []), y, eps)
***** assert (logncdf (x, 0, sqrt(2)*ones (1,5)), y, eps)
***** assert (logncdf (x, zeros (1,5), sqrt(2)), y, eps)
***** assert (logncdf (x, [0 1 NaN 0 1], sqrt(2)), [0 0 NaN y(4:5)], eps)
***** assert (logncdf (x, 0, sqrt(2)*[0 NaN Inf 1 1]), [NaN NaN y(3:5)], eps)
***** assert (logncdf ([x(1:3) NaN x(5)], 0, sqrt(2)), [y(1:3) NaN y(5)], eps)
***** assert (logncdf ([x, NaN], 0, sqrt(2)), [y, NaN], eps)
***** assert (logncdf (single ([x, NaN]), 0, sqrt(2)), single ([y, NaN]), eps ("single"))
***** assert (logncdf ([x, NaN], single (0), sqrt(2)), single ([y, NaN]), eps ("single"))
***** assert (logncdf ([x, NaN], 0, single (sqrt(2))), single ([y, NaN]), eps ("single"))
***** error<logncdf: invalid number of input arguments.> logncdf ()
***** error<logncdf: invalid number of input arguments.> logncdf (1,2,3,4,5,6,7)
***** error<logncdf: invalid argument for upper tail.> logncdf (1, 2, 3, 4, "uper")
***** error<logncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logncdf (ones (3), ones (2), ones (2))
***** error<logncdf: invalid size of covariance matrix.> logncdf (2, 3, 4, [1, 2])
***** error<logncdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = logncdf (1, 2, 3)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (i, 2, 2)
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, i, 2)
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, 2, i)
***** error<logncdf: bad covariance matrix.> ...
 [p, plo, pup] =logncdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/plinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/plinv.m
***** demo
 ## Plot various iCDFs from the Piecewise linear distribution
 p = 0.001:0.001:0.999;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 data1 = plinv (p, x1, Fx1);
 data2 = plinv (p, x2, Fx2);
 plot (p, data1, "-b", p, data2, "-g")
 grid on
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "northwest")
 title ("Piecewise linear iCDF")
 xlabel ("probability")
 ylabel ("values in data")
***** test
 p = 0:0.2:1;
 data = plinv (p, [0, 1], [0, 1]);
 assert (data, p);
***** test
 p = 0:0.2:1;
 data = plinv (p, [0, 2], [0, 1]);
 assert (data, 2 * p);
***** test
 p = 0:0.2:1;
 data_out = 1:6;
 data = plinv (p, [0, 1], [0, 0.5]);
 assert (data, [0, 0.4, 0.8, NA, NA, NA]);
***** test
 p = 0:0.2:1;
 data_out = 1:6;
 data = plinv (p, [0, 0.5], [0, 1]);
 assert (data, [0:0.1:0.5]);
***** error<plinv: function called with too few input arguments.> plinv ()
***** error<plinv: function called with too few input arguments.> plinv (1)
***** error<plinv: function called with too few input arguments.> plinv (1, 2)
***** error<plinv: X and FX must be vectors of equal size.> ...
 plinv (1, [0, 1, 2], [0, 1])
***** error<plinv: X and FX must be at least two-elements long.> ...
 plinv (1, [0], [1])
***** error<plinv: FX must be bounded in the range> ...
 plinv (1, [0, 1, 2], [0, 1, 1.5])
***** error<plinv: FX must be bounded in the range> ...
 plinv (1, [0, 1, 2], [0, i, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (i, [0, 1, 2], [0, 0.5, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (1, [0, i, 2], [0, 0.5, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (1, [0, 1, 2], [0, 0.5i, 1])
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unifpdf.m
***** demo
 ## Plot various PDFs from the continuous uniform distribution
 x = 0:0.001:10;
 y1 = unifpdf (x, 2, 5);
 y2 = unifpdf (x, 3, 9);
 plot (x, y1, "-b", x, y2, "-g")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.4])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northeast")
 title ("Continuous uniform PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2] + 1;
 y = [0 1 1 1 0];
***** assert (unifpdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (unifpdf (x, 1, 2*ones (1,5)), y)
***** assert (unifpdf (x, ones (1,5), 2), y)
***** assert (unifpdf (x, [2 NaN 1 1 1], 2), [NaN NaN y(3:5)])
***** assert (unifpdf (x, 1, 2*[0 NaN 1 1 1]), [NaN NaN y(3:5)])
***** assert (unifpdf ([x, NaN], 1, 2), [y, NaN])
***** assert (unifpdf (x, 0, 1), [1 1 0 0 0])
***** assert (unifpdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (unifpdf (single ([x, NaN]), single (1), 2), single ([y, NaN]))
***** assert (unifpdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<unifpdf: function called with too few input arguments.> unifpdf ()
***** error<unifpdf: function called with too few input arguments.> unifpdf (1)
***** error<unifpdf: function called with too few input arguments.> unifpdf (1, 2)
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (3), ones (2), ones (2))
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (2), ones (3), ones (2))
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (2), ones (2), ones (3))
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (i, 2, 2)
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, i, 2)
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbinrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nbinrnd.m
***** assert (size (nbinrnd (1, 0.5)), [1 1])
***** assert (size (nbinrnd (1, 0.5 * ones (2,1))), [2, 1])
***** assert (size (nbinrnd (1, 0.5 * ones (2,2))), [2, 2])
***** assert (size (nbinrnd (ones (2,1), 0.5)), [2, 1])
***** assert (size (nbinrnd (ones (2,2), 0.5)), [2, 2])
***** assert (size (nbinrnd (1, 0.5, 3)), [3, 3])
***** assert (size (nbinrnd (1, 0.5, [4, 1])), [4, 1])
***** assert (size (nbinrnd (1, 0.5, 4, 1)), [4, 1])
***** assert (size (nbinrnd (1, 0.5, 4, 1, 5)), [4, 1, 5])
***** assert (size (nbinrnd (1, 0.5, 0, 1)), [0, 1])
***** assert (size (nbinrnd (1, 0.5, 1, 0)), [1, 0])
***** assert (size (nbinrnd (1, 0.5, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (nbinrnd (1, 0.5)), "double")
***** assert (class (nbinrnd (1, single (0.5))), "single")
***** assert (class (nbinrnd (1, single ([0.5, 0.5]))), "single")
***** assert (class (nbinrnd (single (1), 0.5)), "single")
***** assert (class (nbinrnd (single ([1, 1]), 0.5)), "single")
***** error<nbinrnd: function called with too few input arguments.> nbinrnd ()
***** error<nbinrnd: function called with too few input arguments.> nbinrnd (1)
***** error<nbinrnd: R and PS must be of common size or scalars.> ...
 nbinrnd (ones (3), ones (2))
***** error<nbinrnd: R and PS must be of common size or scalars.> ...
 nbinrnd (ones (2), ones (3))
***** error<nbinrnd: R and PS must not be complex.> nbinrnd (i, 2, 3)
***** error<nbinrnd: R and PS must not be complex.> nbinrnd (1, i, 3)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, -1)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, 1.2)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, ones (2))
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, [2 -1 2])
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, [2 0 2.5])
***** error<nbinrnd: dimensions must be non-negative integers.> ...
 nbinrnd (1, 2, 2, -1, 5)
***** error<nbinrnd: dimensions must be non-negative integers.> ...
 nbinrnd (1, 2, 2, 1.5, 5)
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), 3)
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), [3, 2])
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gevpdf.m
***** demo
 ## Plot various PDFs from the generalized extreme value distribution
 x = -1:0.001:10;
 y1 = gevpdf (x, 1, 1, 1);
 y2 = gevpdf (x, 0.5, 1, 1);
 y3 = gevpdf (x, 1, 1, 5);
 y4 = gevpdf (x, 1, 2, 5);
 y5 = gevpdf (x, 1, 5, 5);
 y6 = gevpdf (x, 1, 0.5, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 xlim ([-1, 10])
 ylim ([0, 1.1])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "northeast")
 title ("Generalized extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 k = 1;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0.367879   0.143785   0.088569   0.063898   0.049953   0.040997];
 assert (y, expected_y, 0.001);
***** test
 x = -0.5:0.5:2.5;
 sigma = 0.5;
 k = 1;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0 0.735759   0.303265   0.159229   0.097350   0.065498   0.047027];
 assert (y, expected_y, 0.001);
***** test # check for continuity for k near 0
 x = 1;
 sigma = 0.5;
 k = -0.03:0.01:0.03;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0.23820   0.23764   0.23704   0.23641   0.23576   0.23508   0.23438];
 assert (y, expected_y, 0.001);
***** error<gevpdf: function called with too few input arguments.> gevpdf ()
***** error<gevpdf: function called with too few input arguments.> gevpdf (1)
***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2)
***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2, 3)
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (3), ones (2), ones(2), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (3), ones(2), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (2), ones(3), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (2), ones(2), ones(3))
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (i, 2, 3, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, i, 3, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, i, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, 3, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/poissrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/poissrnd.m
***** assert (size (poissrnd (2)), [1, 1])
***** assert (size (poissrnd (ones (2,1))), [2, 1])
***** assert (size (poissrnd (ones (2,2))), [2, 2])
***** assert (size (poissrnd (1, 3)), [3, 3])
***** assert (size (poissrnd (1, [4 1])), [4, 1])
***** assert (size (poissrnd (1, 4, 1)), [4, 1])
***** assert (size (poissrnd (1, 4, 1)), [4, 1])
***** assert (size (poissrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (poissrnd (1, 0, 1)), [0, 1])
***** assert (size (poissrnd (1, 1, 0)), [1, 0])
***** assert (size (poissrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (poissrnd (0, 1, 1), 0)
***** assert (poissrnd ([0, 0, 0], [1, 3]), [0 0 0])
***** assert (class (poissrnd (2)), "double")
***** assert (class (poissrnd (single (2))), "single")
***** assert (class (poissrnd (single ([2 2]))), "single")
***** error<poissrnd: function called with too few input arguments.> poissrnd ()
***** error<poissrnd: LAMBDA must not be complex.> poissrnd (i)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, -1)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, 1.2)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, ones (2))
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, [2 -1 2])
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, [2 0 2.5])
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (ones (2), ones (2))
***** error<poissrnd: dimensions must be non-negative integers.> ...
 poissrnd (1, 2, -1, 5)
***** error<poissrnd: dimensions must be non-negative integers.> ...
 poissrnd (1, 2, 1.5, 5)
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 3)
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), [3, 2])
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hygernd.m
***** assert (size (hygernd (4,2,2)), [1, 1])
***** assert (size (hygernd (4*ones (2,1), 2,2)), [2, 1])
***** assert (size (hygernd (4*ones (2,2), 2,2)), [2, 2])
***** assert (size (hygernd (4, 2*ones (2,1), 2)), [2, 1])
***** assert (size (hygernd (4, 2*ones (2,2), 2)), [2, 2])
***** assert (size (hygernd (4, 2, 2*ones (2,1))), [2, 1])
***** assert (size (hygernd (4, 2, 2*ones (2,2))), [2, 2])
***** assert (size (hygernd (4, 2, 2, 3)), [3, 3])
***** assert (size (hygernd (4, 2, 2, [4 1])), [4, 1])
***** assert (size (hygernd (4, 2, 2, 4, 1)), [4, 1])
***** assert (class (hygernd (4,2,2)), "double")
***** assert (class (hygernd (single (4),2,2)), "single")
***** assert (class (hygernd (single ([4 4]),2,2)), "single")
***** assert (class (hygernd (4,single (2),2)), "single")
***** assert (class (hygernd (4,single ([2 2]),2)), "single")
***** assert (class (hygernd (4,2,single (2))), "single")
***** assert (class (hygernd (4,2,single ([2 2]))), "single")
***** error<hygernd: function called with too few input arguments.> hygernd ()
***** error<hygernd: function called with too few input arguments.> hygernd (1)
***** error<hygernd: function called with too few input arguments.> hygernd (1, 2)
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (3), ones (2), ones (2))
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (2), ones (3), ones (2))
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (2), ones (2), ones (3))
***** error<hygernd: T, M, and N must not be complex.> hygernd (i, 2, 3)
***** error<hygernd: T, M, and N must not be complex.> hygernd (1, i, 3)
***** error<hygernd: T, M, and N must not be complex.> hygernd (1, 2, i)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, -1)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, 1.2)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, ones (2))
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, [2 -1 2])
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, [2 0 2.5])
***** error<hygernd: dimensions must be non-negative integers.> ...
 hygernd (1, 2, 3, 2, -1, 5)
***** error<hygernd: dimensions must be non-negative integers.> ...
 hygernd (1, 2, 3, 2, 1.5, 5)
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, 3)
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, [3, 2])
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, 3, 2)
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_fun/binoinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/binoinv.m
***** demo
 ## Plot various iCDFs from the binomial distribution
 p = 0.001:0.001:0.999;
 x1 = binoinv (p, 20, 0.5);
 x2 = binoinv (p, 20, 0.7);
 x3 = binoinv (p, 40, 0.5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "southeast")
 title ("Binomial iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of successes)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (binoinv (p, 2*ones (1,5), 0.5*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2, 0.5*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2*ones (1,5), 0.5), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2*[0 -1 NaN 1.1 1], 0.5), [NaN NaN NaN NaN NaN])
***** assert (binoinv (p, 2, 0.5*[0 -1 NaN 3 1]), [NaN NaN NaN NaN NaN])
***** assert (binoinv ([p(1:2) NaN p(4:5)], 2, 0.5), [NaN 0 NaN 2 NaN])
***** assert (binoinv ([p, NaN], 2, 0.5), [NaN 0 1 2 NaN NaN])
***** assert (binoinv (single ([p, NaN]), 2, 0.5), single ([NaN 0 1 2 NaN NaN]))
***** assert (binoinv ([p, NaN], single (2), 0.5), single ([NaN 0 1 2 NaN NaN]))
***** assert (binoinv ([p, NaN], 2, single (0.5)), single ([NaN 0 1 2 NaN NaN]))
***** shared x, tol
 x = magic (3) + 1;
 tol = 1;
***** assert (binoinv (binocdf (1:10, 11, 0.1), 11, 0.1), 1:10, tol)
***** assert (binoinv (binocdf (1:10, 2*(1:10), 0.1), 2*(1:10), 0.1), 1:10, tol)
***** assert (binoinv (binocdf (x, 2*x, 1./x), 2*x, 1./x), x, tol)
***** error<binoinv: function called with too few input arguments.> binoinv ()
***** error<binoinv: function called with too few input arguments.> binoinv (1)
***** error<binoinv: function called with too few input arguments.> binoinv (1,2)
***** error<binoinv: function called with too many inputs> binoinv (1,2,3,4)
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (3), ones (2), ones (2))
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (2), ones (3), ones (2))
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (2), ones (2), ones (3))
***** error<binoinv: P, N, and PS must not be complex.> binoinv (i, 2, 2)
***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, i, 2)
***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, 2, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wblrnd.m
***** assert (size (wblrnd (1, 1)), [1 1])
***** assert (size (wblrnd (1, ones (2,1))), [2, 1])
***** assert (size (wblrnd (1, ones (2,2))), [2, 2])
***** assert (size (wblrnd (ones (2,1), 1)), [2, 1])
***** assert (size (wblrnd (ones (2,2), 1)), [2, 2])
***** assert (size (wblrnd (1, 1, 3)), [3, 3])
***** assert (size (wblrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (wblrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (wblrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (wblrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (wblrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (wblrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (wblrnd (1, 1)), "double")
***** assert (class (wblrnd (1, single (1))), "single")
***** assert (class (wblrnd (1, single ([1, 1]))), "single")
***** assert (class (wblrnd (single (1), 1)), "single")
***** assert (class (wblrnd (single ([1, 1]), 1)), "single")
***** error<wblrnd: function called with too few input arguments.> wblrnd ()
***** error<wblrnd: function called with too few input arguments.> wblrnd (1)
***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
 wblrnd (ones (3), ones (2))
***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
 wblrnd (ones (2), ones (3))
***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (i, 2, 3)
***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (1, i, 3)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, -1)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, 1.2)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, ones (2))
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, [2 -1 2])
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, [2 0 2.5])
***** error<wblrnd: dimensions must be non-negative integers.> ...
 wblrnd (1, 2, 2, -1, 5)
***** error<wblrnd: dimensions must be non-negative integers.> ...
 wblrnd (1, 2, 2, 1.5, 5)
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), 3)
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), [3, 2])
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gumbelrnd.m
***** assert (size (gumbelrnd (1, 1)), [1 1])
***** assert (size (gumbelrnd (1, ones (2,1))), [2, 1])
***** assert (size (gumbelrnd (1, ones (2,2))), [2, 2])
***** assert (size (gumbelrnd (ones (2,1), 1)), [2, 1])
***** assert (size (gumbelrnd (ones (2,2), 1)), [2, 2])
***** assert (size (gumbelrnd (1, 1, 3)), [3, 3])
***** assert (size (gumbelrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (gumbelrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (gumbelrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (gumbelrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (gumbelrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (gumbelrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (gumbelrnd (1, 1)), "double")
***** assert (class (gumbelrnd (1, single (1))), "single")
***** assert (class (gumbelrnd (1, single ([1, 1]))), "single")
***** assert (class (gumbelrnd (single (1), 1)), "single")
***** assert (class (gumbelrnd (single ([1, 1]), 1)), "single")
***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd ()
***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd (1)
***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
 gumbelrnd (ones (3), ones (2))
***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
 gumbelrnd (ones (2), ones (3))
***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (i, 2, 3)
***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (1, i, 3)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, -1)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, 1.2)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, ones (2))
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, [2 -1 2])
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, [2 0 2.5])
***** error<gumbelrnd: dimensions must be non-negative integers.> ...
 gumbelrnd (1, 2, 2, -1, 5)
***** error<gumbelrnd: dimensions must be non-negative integers.> ...
 gumbelrnd (1, 2, 2, 1.5, 5)
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), 3)
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), [3, 2])
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/copulacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/copulacdf.m
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 p = copulacdf ("Clayton", x, theta);
 expected_p = [0.1395; 0.1767];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 p = copulacdf ("Gumbel", x, 2);
 expected_p = [0.1464; 0.1464];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 p = copulacdf ("Frank", x, theta);
 expected_p = [0.0699; 0.0930];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [0.3; 0.7];
 p = copulacdf ("AMH", x, theta);
 expected_p = [0.0629; 0.0959];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.1:0.4];
 theta = [0.2, 0.1, 0.1, 0.05];
 p = copulacdf ("FGM", x, theta);
 expected_p = [0.0558; 0.0293];
 assert (p, expected_p, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unifrnd.m
***** assert (size (unifrnd (1, 1)), [1 1])
***** assert (size (unifrnd (1, ones (2,1))), [2, 1])
***** assert (size (unifrnd (1, ones (2,2))), [2, 2])
***** assert (size (unifrnd (ones (2,1), 1)), [2, 1])
***** assert (size (unifrnd (ones (2,2), 1)), [2, 2])
***** assert (size (unifrnd (1, 1, 3)), [3, 3])
***** assert (size (unifrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (unifrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (unifrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (unifrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (unifrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (unifrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (unifrnd (1, 1)), "double")
***** assert (class (unifrnd (1, single (1))), "single")
***** assert (class (unifrnd (1, single ([1, 1]))), "single")
***** assert (class (unifrnd (single (1), 1)), "single")
***** assert (class (unifrnd (single ([1, 1]), 1)), "single")
***** error<unifrnd: function called with too few input arguments.> unifrnd ()
***** error<unifrnd: function called with too few input arguments.> unifrnd (1)
***** error<unifrnd: A and B must be of common size or scalars.> ...
 unifrnd (ones (3), ones (2))
***** error<unifrnd: A and B must be of common size or scalars.> ...
 unifrnd (ones (2), ones (3))
***** error<unifrnd: A and B must not be complex.> unifrnd (i, 2, 3)
***** error<unifrnd: A and B must not be complex.> unifrnd (1, i, 3)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, -1)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, 1.2)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, ones (2))
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, [2 -1 2])
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, [2 0 2.5])
***** error<unifrnd: dimensions must be non-negative integers.> ...
 unifrnd (1, 2, 2, -1, 5)
***** error<unifrnd: dimensions must be non-negative integers.> ...
 unifrnd (1, 2, 2, 1.5, 5)
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), 3)
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), [3, 2])
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/betainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/betainv.m
***** demo
 ## Plot various iCDFs from the Beta distribution
 p = 0.001:0.001:0.999;
 x1 = betainv (p, 0.5, 0.5);
 x2 = betainv (p, 5, 1);
 x3 = betainv (p, 1, 3);
 x4 = betainv (p, 2, 2);
 x5 = betainv (p, 2, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "southeast")
 title ("Beta iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (betainv (p, ones (1,5), 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, 1, 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, ones (1,5), 2), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, [1 0 NaN 1 1], 2), [NaN NaN NaN 1 NaN])
***** assert (betainv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 1 NaN])
***** assert (betainv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN 1 NaN])
***** assert (betainv ([p, NaN], 1, 2), [NaN 0 0.5 1 NaN NaN], eps)
***** assert (betainv (single ([p, NaN]), 1, 2), single ([NaN 0 0.5 1 NaN NaN]))
***** assert (betainv ([p, NaN], single (1), 2), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
***** assert (betainv ([p, NaN], 1, single (2)), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
***** error<betainv: function called with too few input arguments.> betainv ()
***** error<betainv: function called with too few input arguments.> betainv (1)
***** error<betainv: function called with too few input arguments.> betainv (1,2)
***** error<betainv: function called with too many inputs> betainv (1,2,3,4)
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (3), ones (2), ones (2))
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (2), ones (3), ones (2))
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (2), ones (2), ones (3))
***** error<betainv: P, A, and B must not be complex.> betainv (i, 2, 2)
***** error<betainv: P, A, and B must not be complex.> betainv (2, i, 2)
***** error<betainv: P, A, and B must not be complex.> betainv (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/exprnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/exprnd.m
***** assert (size (exprnd (2)), [1, 1])
***** assert (size (exprnd (ones (2,1))), [2, 1])
***** assert (size (exprnd (ones (2,2))), [2, 2])
***** assert (size (exprnd (1, 3)), [3, 3])
***** assert (size (exprnd (1, [4 1])), [4, 1])
***** assert (size (exprnd (1, 4, 1)), [4, 1])
***** assert (size (exprnd (1, 4, 1)), [4, 1])
***** assert (size (exprnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (exprnd (1, 0, 1)), [0, 1])
***** assert (size (exprnd (1, 1, 0)), [1, 0])
***** assert (size (exprnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (exprnd (2)), "double")
***** assert (class (exprnd (single (2))), "single")
***** assert (class (exprnd (single ([2 2]))), "single")
***** error<exprnd: function called with too few input arguments.> exprnd ()
***** error<exprnd: MU must not be complex.> exprnd (i)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, -1)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, 1.2)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, ones (2))
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, [2 -1 2])
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, [2 0 2.5])
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (ones (2), ones (2))
***** error<exprnd: dimensions must be non-negative integers.> ...
 exprnd (1, 2, -1, 5)
***** error<exprnd: dimensions must be non-negative integers.> ...
 exprnd (1, 2, 1.5, 5)
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 3)
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), [3, 2])
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 2, 3)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/hnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hnpdf.m
***** demo
 ## Plot various PDFs from the half-normal distribution
 x = 0:0.001:10;
 y1 = hnpdf (x, 0, 1);
 y2 = hnpdf (x, 0, 2);
 y3 = hnpdf (x, 0, 3);
 y4 = hnpdf (x, 0, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.9])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northeast")
 title ("Half-normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Plot half-normal against normal probability density function
 x = -5:0.001:5;
 y1 = hnpdf (x, 0, 1);
 y2 = normpdf (x);
 plot (x, y1, "-b", x, y2, "-g")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.9])
 legend ({"half-normal with μ = 0, σ = 1", ...
          "standart normal (μ = 0, σ = 1)"}, "location", "northeast")
 title ("Half-normal against standard normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0, 0.7979, 0.7041, 0.4839, 0];
***** assert (hnpdf ([x, NaN], 0, 1), [y, NaN], 1e-4)
***** assert (hnpdf (x, 0, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
***** error<hnpdf: function called with too few input arguments.> hnpdf ()
***** error<hnpdf: function called with too few input arguments.> hnpdf (1)
***** error<hnpdf: function called with too few input arguments.> hnpdf (1, 2)
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (1, ones (2), ones (3))
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (ones (2), 1, ones (3))
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (ones (2), ones (3), 1)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (i, 2, 3)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, i, 3)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbincdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nbincdf.m
***** demo
 ## Plot various CDFs from the negative binomial distribution
 x = 0:50;
 p1 = nbincdf (x, 2, 0.15);
 p2 = nbincdf (x, 5, 0.2);
 p3 = nbincdf (x, 4, 0.4);
 p4 = nbincdf (x, 10, 0.3);
 plot (x, p1, "*r", x, p2, "*g", x, p3, "*k", x, p4, "*m")
 grid on
 xlim ([0, 40])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "southeast")
 title ("Negative binomial CDF")
 xlabel ("values in x (number of failures)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0 1/2 3/4 7/8 1];
***** assert (nbincdf (x, ones (1,5), 0.5*ones (1,5)), y)
***** assert (nbincdf (x, 1, 0.5*ones (1,5)), y)
***** assert (nbincdf (x, ones (1,5), 0.5), y)
***** assert (nbincdf (x, ones (1,5), 0.5, "upper"), 1 - y, eps)
***** assert (nbincdf ([x(1:3) 0 x(5)], [0 1 NaN 1.5 Inf], 0.5), ...
 [NaN 1/2 NaN nbinpdf(0,1.5,0.5) NaN], eps)
***** assert (nbincdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
***** assert (nbincdf ([x(1:2) NaN x(4:5)], 1, 0.5), [y(1:2) NaN y(4:5)])
***** assert (nbincdf ([x, NaN], 1, 0.5), [y, NaN])
***** assert (nbincdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
***** assert (nbincdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
***** assert (nbincdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
***** error<nbincdf: function called with too few input arguments.> nbincdf ()
***** error<nbincdf: function called with too few input arguments.> nbincdf (1)
***** error<nbincdf: function called with too few input arguments.> nbincdf (1, 2)
***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, 4)
***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, "some")
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (3), ones (2), ones (2))
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (2), ones (3), ones (2))
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (2), ones (2), ones (3))
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (i, 2, 2)
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, i, 2)
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, 2, i)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unidpdf.m
***** demo
 ## Plot various PDFs from the discrete uniform distribution
 x = 0:10;
 y1 = unidpdf (x, 5);
 y2 = unidpdf (x, 9);
 plot (x, y1, "*b", x, y2, "*g")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.25])
 legend ({"N = 5", "N = 9"}, "location", "northeast")
 title ("Descrete uniform PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 10 11];
 y = [0 0 0.1 0.1 0.1 0];
***** assert (unidpdf (x, 10*ones (1,6)), y)
***** assert (unidpdf (x, 10), y)
***** assert (unidpdf (x, 10*[0 NaN 1 1 1 1]), [NaN NaN y(3:6)])
***** assert (unidpdf ([x, NaN], 10), [y, NaN])
***** assert (unidpdf (single ([x, NaN]), 10), single ([y, NaN]))
***** assert (unidpdf ([x, NaN], single (10)), single ([y, NaN]))
***** error<unidpdf: function called with too few input arguments.> unidpdf ()
***** error<unidpdf: function called with too few input arguments.> unidpdf (1)
***** error<unidpdf: X and N must be of common size or scalars.> ...
 unidpdf (ones (3), ones (2))
***** error<unidpdf: X and N must be of common size or scalars.> ...
 unidpdf (ones (2), ones (3))
***** error<unidpdf: X and N must not be complex.> unidpdf (i, 2)
***** error<unidpdf: X and N must not be complex.> unidpdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/loglcdf.m
***** demo
 ## Plot various CDFs from the log-logistic distribution
 x = 0:0.001:2;
 p1 = loglcdf (x, log (1), 1/0.5);
 p2 = loglcdf (x, log (1), 1);
 p3 = loglcdf (x, log (1), 1/2);
 p4 = loglcdf (x, log (1), 1/4);
 p5 = loglcdf (x, log (1), 1/8);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
 grid on
 title ("Log-logistic CDF")
 xlabel ("values in x")
 ylabel ("probability")
 text (0.05, 0.64, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared out1, out2
 out1 = [0, 0.5, 0.66666667, 0.75, 0.8, 0.83333333];
 out2 = [0, 0.4174, 0.4745, 0.5082, 0.5321, 0.5506];
***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
***** assert (loglcdf ([0:5], 1, 3), out2, 1e-4)
***** assert (loglcdf ([0:5], 1, 3, "upper"), 1 - out2, 1e-4)
***** assert (class (loglcdf (single (1), 2, 3)), "single")
***** assert (class (loglcdf (1, single (2), 3)), "single")
***** assert (class (loglcdf (1, 2, single (3))), "single")
***** error<loglcdf: function called with too few input arguments.> loglcdf (1)
***** error<loglcdf: function called with too few input arguments.> loglcdf (1, 2)
***** error<loglcdf: invalid argument for upper tail.> ...
 loglcdf (1, 2, 3, 4)
***** error<loglcdf: invalid argument for upper tail.> ...
 loglcdf (1, 2, 3, "uper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (1, ones (2), ones (3))
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (1, ones (2), ones (3), "upper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), 1, ones (3))
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), 1, ones (3), "upper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), ones (3), 1)
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), ones (3), 1, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i, "upper")
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/normpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/normpdf.m
***** demo
 ## Plot various PDFs from the normal distribution
 x = -5:0.01:5;
 y1 = normpdf (x, 0, 0.5);
 y2 = normpdf (x, 0, 1);
 y3 = normpdf (x, 0, 2);
 y4 = normpdf (x, -2, 0.8);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.9])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northeast")
 title ("Normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = 1 / sqrt (2 * pi) * exp (-(x - 1) .^ 2 / 2);
***** assert (normpdf (x, ones (1,4), ones (1,4)), y, eps)
***** assert (normpdf (x, 1, ones (1,4)), y, eps)
***** assert (normpdf (x, ones (1,4), 1), y, eps)
***** assert (normpdf (x, [0 -Inf NaN Inf], 1), [y(1) NaN NaN NaN], eps)
***** assert (normpdf (x, 1, [Inf NaN -1 0]), [NaN NaN NaN NaN], eps)
***** assert (normpdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (normpdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (normpdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (normpdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<normpdf: function called with too few input arguments.> normpdf ()
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (3), ones (2), ones (2))
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (2), ones (3), ones (2))
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (2), ones (2), ones (3))
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (i, 2, 2)
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, i, 2)
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/burrcdf.m
***** demo
 ## Plot various CDFs from the Burr type XII distribution
 x = 0.001:0.001:5;
 p1 = burrcdf (x, 1, 1, 1);
 p2 = burrcdf (x, 1, 1, 2);
 p3 = burrcdf (x, 1, 1, 3);
 p4 = burrcdf (x, 1, 2, 1);
 p5 = burrcdf (x, 1, 3, 1);
 p6 = burrcdf (x, 1, 0.5, 2);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "southeast")
 title ("Burr type XII CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 1/2, 2/3, 1];
***** assert (burrcdf (x, ones(1,5), ones (1,5), ones (1,5)), y, eps)
***** assert (burrcdf (x, 1, 1, 1), y, eps)
***** assert (burrcdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf ([x, NaN], 1, 1, 1), [y, NaN], eps)
***** assert (burrcdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
***** error<burrcdf: function called with too few input arguments.> burrcdf ()
***** error<burrcdf: function called with too few input arguments.> burrcdf (1)
***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2)
***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2, 3)
***** error<burrcdf: function called with too many inputs> ...
 burrcdf (1, 2, 3, 4, 5, 6)
***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, "tail")
***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, 5)
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (3), ones (2), ones(2), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (3), ones(2), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (2), ones(3), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (2), ones(2), ones(3))
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (i, 2, 3, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, i, 3, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, i, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, 3, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nakainv.m
***** demo
 ## Plot various iCDFs from the Nakagami distribution
 p = 0.001:0.001:0.999;
 x1 = nakainv (p, 0.5, 1);
 x2 = nakainv (p, 1, 1);
 x3 = nakainv (p, 1, 2);
 x4 = nakainv (p, 1, 3);
 x5 = nakainv (p, 2, 1);
 x6 = nakainv (p, 2, 2);
 x7 = nakainv (p, 5, 1);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
       p, x5, "-k", p, x6, "-b", p, x7, "-c")
 grid on
 ylim ([0, 3])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "northwest")
 title ("Nakagami iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
 y = [NaN, NaN, 0, 0.83255461115769769, Inf, NaN, NaN];
***** assert (nakainv (p, ones (1,7), ones (1,7)), y, eps)
***** assert (nakainv (p, 1, 1), y, eps)
***** assert (nakainv (p, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (nakainv (p, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
***** assert (nakainv ([p, NaN], 1, 1), [y, NaN], eps)
***** assert (nakainv (single ([p, NaN]), 1, 1), single ([y, NaN]))
***** assert (nakainv ([p, NaN], single (1), 1), single ([y, NaN]))
***** assert (nakainv ([p, NaN], 1, single (1)), single ([y, NaN]))
***** error<nakainv: function called with too few input arguments.> nakainv ()
***** error<nakainv: function called with too few input arguments.> nakainv (1)
***** error<nakainv: function called with too few input arguments.> nakainv (1, 2)
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (3), ones (2), ones(2))
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (2), ones (3), ones(2))
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (2), ones (2), ones(3))
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (i, 4, 3)
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, i, 3)
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, 4, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlsrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tlsrnd.m
***** assert (size (tlsrnd (1, 2, 3)), [1, 1])
***** assert (size (tlsrnd (ones (2,1), 2, 3)), [2, 1])
***** assert (size (tlsrnd (ones (2,2), 2, 3)), [2, 2])
***** assert (size (tlsrnd (1, 2, 3, 3)), [3, 3])
***** assert (size (tlsrnd (1, 2, 3, [4 1])), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1, 5)), [4, 1, 5])
***** assert (size (tlsrnd (1, 2, 3, 0, 1)), [0, 1])
***** assert (size (tlsrnd (1, 2, 3, 1, 0)), [1, 0])
***** assert (size (tlsrnd (1, 2, 3, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (tlsrnd (1, 2, 0, 1, 1), NaN)
***** assert (tlsrnd (1, 2, [0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (tlsrnd (1, 2, 3)), "double")
***** assert (class (tlsrnd (single (1), 2, 3)), "single")
***** assert (class (tlsrnd (single ([1, 1]), 2, 3)), "single")
***** assert (class (tlsrnd (1, single (2), 3)), "single")
***** assert (class (tlsrnd (1, single ([2, 2]), 3)), "single")
***** assert (class (tlsrnd (1, 2, single (3))), "single")
***** assert (class (tlsrnd (1, 2, single ([3, 3]))), "single")
***** error<tlsrnd: function called with too few input arguments.> tlsrnd ()
***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1)
***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1, 2)
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (ones (3), ones (2), 1)
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (ones (2), 1, ones (3))
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (1, ones (2), ones (3))
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (i, 2, 3)
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, i, 3)
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, 2, i)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, -1)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, 1.2)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, ones (2))
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, [2 -1 2])
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, [2 0 2.5])
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (ones (2), 2, 3, ones (2))
***** error<tlsrnd: dimensions must be non-negative integers.> ...
 tlsrnd (1, 2, 3, 2, -1, 5)
***** error<tlsrnd: dimensions must be non-negative integers.> ...
 tlsrnd (1, 2, 3, 2, 1.5, 5)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (ones (2,2), 2, 3, 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, ones (2,2), 3, 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), [3, 3])
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), 2, 3)
42 tests, 42 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hygepdf.m
***** demo
 ## Plot various PDFs from the hypergeometric distribution
 x = 0:60;
 y1 = hygepdf (x, 500, 50, 100);
 y2 = hygepdf (x, 500, 60, 200);
 y3 = hygepdf (x, 500, 70, 300);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 xlim ([0, 60])
 ylim ([0, 0.18])
 legend ({"m = 500, k = 50, μ = 100", "m = 500, k = 60, μ = 200", ...
          "m = 500, k = 70, μ = 300"}, "location", "northeast")
 title ("Hypergeometric PDF")
 xlabel ("values in x (number of successes)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/6 4/6 1/6 0];
***** assert (hygepdf (x, 4*ones (1,5), 2, 2), y, eps)
***** assert (hygepdf (x, 4, 2*ones (1,5), 2), y, eps)
***** assert (hygepdf (x, 4, 2, 2*ones (1,5)), y, eps)
***** assert (hygepdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [0 NaN NaN NaN 0], eps)
***** assert (hygepdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [0 NaN NaN NaN 0], eps)
***** assert (hygepdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN], eps)
***** assert (hygepdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [0 NaN NaN NaN 0], eps)
***** assert (hygepdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN], eps)
***** assert (hygepdf ([x, NaN], 4, 2, 2), [y, NaN], eps)
***** assert (hygepdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), eps("single"))
***** assert (hygepdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), eps("single"))
***** assert (hygepdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), eps("single"))
***** assert (hygepdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), eps("single"))
***** test
 z = zeros(3,5);
 z([4,5,6,8,9,12]) = [1, 0.5, 1/6, 0.5, 2/3, 1/6];
 assert (hygepdf (x, 4, [0, 1, 2], 2, "vectorexpand"), z, eps);
 assert (hygepdf (x, 4, [0, 1, 2]', 2, "vectorexpand"), z, eps);
 assert (hygepdf (x', 4, [0, 1, 2], 2, "vectorexpand"), z, eps);
 assert (hygepdf (2, 4, [0 ,1, 2], 2, "vectorexpand"), z(:,4), eps);
 assert (hygepdf (x, 4, 1, 2, "vectorexpand"), z(2,:), eps);
 assert (hygepdf ([NaN, x], 4, [0 1 2]', 2, "vectorexpand"), [NaN(3,1), z], eps);
***** error<hygepdf: function called with too few input arguments.> hygepdf ()
***** error<hygepdf: function called with too few input arguments.> hygepdf (1)
***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2)
***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2,3)
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (3), ones (2), ones (2))
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (2), ones (3), ones (2))
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (2), ones (2), ones (3))
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (i, 2, 2, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, i, 2, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, i, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/expinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/expinv.m
***** demo
 ## Plot various iCDFs from the exponential distribution
 p = 0.001:0.001:0.999;
 x1 = expinv (p, 2/3);
 x2 = expinv (p, 1.0);
 x3 = expinv (p, 2.0);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 5])
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northwest")
 title ("Exponential iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.3934693402873666 1 2];
***** assert (expinv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (expinv (p, 2), [NaN 0 1 Inf NaN], eps)
***** assert (expinv (p, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (expinv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], eps)
***** assert (expinv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], eps)
***** assert (expinv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), eps)
***** assert (expinv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), eps)
***** error<expinv: invalid number of input arguments.> expinv ()
***** error<expinv: invalid number of input arguments.> expinv (1, 2 ,3 ,4 ,5)
***** error<expinv: P and MU must be of common size or scalars.> ...
 expinv (ones (3), ones (2))
***** error<expinv: invalid size of variance, PCOV must be a scalar.> ...
 expinv (2, 3, [1, 2])
***** error<expinv: variance, PCOV, is required for confidence bounds.> ...
 [x, xlo, xup] = expinv (1, 2)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, 0)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, 1.22)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, [0.05, 0.1])
***** error<expinv: P and MU must not be complex.> expinv (i, 2)
***** error<expinv: P and MU must not be complex.> expinv (2, i)
***** error<expinv: variance, PCOV, cannot be negative.> ...
 [x, xlo, xup] = expinv (1, 2, -1, 0.04)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unidcdf.m
***** demo
 ## Plot various CDFs from the discrete uniform distribution
 x = 0:10;
 p1 = unidcdf (x, 5);
 p2 = unidcdf (x, 9);
 plot (x, p1, "*b", x, p2, "*g")
 grid on
 xlim ([0, 10])
 ylim ([0, 1])
 legend ({"N = 5", "N = 9"}, "location", "southeast")
 title ("Discrete uniform CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [0 1 2.5 10 11];
 y = [0, 0.1 0.2 1.0 1.0];
***** assert (unidcdf (x, 10*ones (1,5)), y)
***** assert (unidcdf (x, 10*ones (1,5), "upper"), 1 - y)
***** assert (unidcdf (x, 10), y)
***** assert (unidcdf (x, 10, "upper"), 1 - y)
***** assert (unidcdf (x, 10*[0 1 NaN 1 1]), [NaN 0.1 NaN y(4:5)])
***** assert (unidcdf ([x(1:2) NaN Inf x(5)], 10), [y(1:2) NaN 1 y(5)])
***** assert (unidcdf ([x, NaN], 10), [y, NaN])
***** assert (unidcdf (single ([x, NaN]), 10), single ([y, NaN]))
***** assert (unidcdf ([x, NaN], single (10)), single ([y, NaN]))
***** error<unidcdf: function called with too few input arguments.> unidcdf ()
***** error<unidcdf: function called with too few input arguments.> unidcdf (1)
***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, 3)
***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, "tail")
***** error<unidcdf: X and N must be of common size or scalars.> ...
 unidcdf (ones (3), ones (2))
***** error<unidcdf: X and N must be of common size or scalars.> ...
 unidcdf (ones (2), ones (3))
***** error<unidcdf: X and N must not be complex.> unidcdf (i, 2)
***** error<unidcdf: X and N must not be complex.> unidcdf (2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/iwishpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/iwishpdf.m
***** assert(iwishpdf(4, 3, 3.1), 0.04226595, 1E-7);
***** assert(iwishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 1.60166e-05, 1E-10);
***** assert(iwishpdf([6 2 5; 2 10 -5; 5 -5 25], ...
 [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.946831e-12, 1E-17);
***** error iwishpdf ()
***** error iwishpdf (1, 2)
***** error iwishpdf (1, 2, 0)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dist_fun/frnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/frnd.m
***** assert (size (frnd (1, 1)), [1 1])
***** assert (size (frnd (1, ones (2,1))), [2, 1])
***** assert (size (frnd (1, ones (2,2))), [2, 2])
***** assert (size (frnd (ones (2,1), 1)), [2, 1])
***** assert (size (frnd (ones (2,2), 1)), [2, 2])
***** assert (size (frnd (1, 1, 3)), [3, 3])
***** assert (size (frnd (1, 1, [4, 1])), [4, 1])
***** assert (size (frnd (1, 1, 4, 1)), [4, 1])
***** assert (size (frnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (frnd (1, 1, 0, 1)), [0, 1])
***** assert (size (frnd (1, 1, 1, 0)), [1, 0])
***** assert (size (frnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (frnd (1, 1)), "double")
***** assert (class (frnd (1, single (1))), "single")
***** assert (class (frnd (1, single ([1, 1]))), "single")
***** assert (class (frnd (single (1), 1)), "single")
***** assert (class (frnd (single ([1, 1]), 1)), "single")
***** error<frnd: function called with too few input arguments.> frnd ()
***** error<frnd: function called with too few input arguments.> frnd (1)
***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
 frnd (ones (3), ones (2))
***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
 frnd (ones (2), ones (3))
***** error<frnd: DF1 and DF2 must not be complex.> frnd (i, 2, 3)
***** error<frnd: DF1 and DF2 must not be complex.> frnd (1, i, 3)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, -1)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, 1.2)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, ones (2))
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, [2 -1 2])
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, [2 0 2.5])
***** error<frnd: dimensions must be non-negative integers.> ...
 frnd (1, 2, 2, -1, 5)
***** error<frnd: dimensions must be non-negative integers.> ...
 frnd (1, 2, 2, 1.5, 5)
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), 3)
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), [3, 2])
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/binornd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/binornd.m
***** assert (size (binornd (2, 1/2)), [1 1])
***** assert (size (binornd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (binornd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (binornd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (binornd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (binornd (ones (2, 1), 1)), [2, 1])
***** assert (size (binornd (ones (2, 2), 1)), [2, 2])
***** assert (size (binornd (2, 1/2, 3)), [3, 3])
***** assert (size (binornd (1, 1, [4, 1])), [4, 1])
***** assert (size (binornd (1, 1, 4, 1)), [4, 1])
***** assert (size (binornd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (binornd (1, 1, 0, 1)), [0, 1])
***** assert (size (binornd (1, 1, 1, 0)), [1, 0])
***** assert (size (binornd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (binornd (1, 1)), "double")
***** assert (class (binornd (1, single (0))), "single")
***** assert (class (binornd (1, single ([0, 0]))), "single")
***** assert (class (binornd (1, single (1), 2)), "single")
***** assert (class (binornd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (binornd (single (1), 1, 2)), "single")
***** assert (class (binornd (single ([1, 1]), 1, 1, 2)), "single")
***** error<binornd: function called with too few input arguments.> binornd ()
***** error<binornd: function called with too few input arguments.> binornd (1)
***** error<binornd: N and PS must be of common size or scalars.> ...
 binornd (ones (3), ones (2))
***** error<binornd: N and PS must be of common size or scalars.> ...
 binornd (ones (2), ones (3))
***** error<binornd: N and PS must not be complex.> binornd (i, 2)
***** error<binornd: N and PS must not be complex.> binornd (1, i)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, -1)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, 1.2)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, ones (2))
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, [2 -1 2])
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, [2 0 2.5])
***** error<binornd: dimensions must be non-negative integers.> ...
 binornd (1, 1/2, 2, -1, 5)
***** error<binornd: dimensions must be non-negative integers.> ...
 binornd (1, 1/2, 2, 1.5, 5)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), 3)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), [3, 2])
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), 3, 2)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, 3)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, [3, 2])
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, 3, 2)
40 tests, 40 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nctpdf.m
***** demo
 ## Plot various PDFs from the noncentral T distribution
 x = -5:0.01:10;
 y1 = nctpdf (x, 1, 0);
 y2 = nctpdf (x, 4, 0);
 y3 = nctpdf (x, 1, 2);
 y4 = nctpdf (x, 4, 2);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
 grid on
 xlim ([-5, 10])
 ylim ([0, 0.4])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northeast")
 title ("Noncentral T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral T PDF with MU = 1 to the T PDF
 ## with the same number of degrees of freedom (10).

 x = -5:0.1:5;
 y1 = nctpdf (x, 10, 1);
 y2 = tpdf (x, 10);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.4])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral T vs T PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df, mu
 x1 = [-Inf, 2, NaN, 4, Inf];
 df = [2, 0, -1, 1, 4];
 mu = [1, NaN, 3, -1, 2];
***** assert (nctpdf (x1, df, mu), [0, NaN, NaN, 0.00401787561306999, 0], 1e-14);
***** assert (nctpdf (x1, df, 1), [0, NaN, NaN, 0.0482312135423008, 0], 1e-14);
***** assert (nctpdf (x1, df, 3), [0, NaN, NaN, 0.1048493126401585, 0], 1e-14);
***** assert (nctpdf (x1, df, 2), [0, NaN, NaN, 0.08137377919890307, 0], 1e-14);
***** assert (nctpdf (x1, 3, mu), [0, NaN, NaN, 0.001185305171654381, 0], 1e-14);
***** assert (nctpdf (2, df, mu), [0.1791097459405861, NaN, NaN, ...
                             0.0146500727180389, 0.3082302682110299], 1e-14);
***** assert (nctpdf (4, df, mu), [0.04467929612254971, NaN, NaN, ...
                             0.00401787561306999, 0.0972086534042828], 1e-14);
***** error<nctpdf: function called with too few input arguments.> nctpdf ()
***** error<nctpdf: function called with too few input arguments.> nctpdf (1)
***** error<nctpdf: function called with too few input arguments.> nctpdf (1, 2)
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (3), ones (2), ones (2))
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (2), ones (3), ones (2))
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (2), ones (2), ones (3))
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (i, 2, 2)
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, i, 2)
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlsinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tlsinv.m
***** demo
 ## Plot various iCDFs from the location-scale Student's T distribution
 p = 0.001:0.001:0.999;
 x1 = tlsinv (p, 0, 1, 1);
 x2 = tlsinv (p, 0, 2, 2);
 x3 = tlsinv (p, 3, 2, 5);
 x4 = tlsinv (p, -1, 3, Inf);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
 grid on
 xlim ([0, 1])
 ylim ([-8, 8])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "southeast")
 title ("Location-scale Student's T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (tlsinv (p, 0, 1, ones (1,5)), [NaN -Inf 0 Inf NaN])
***** assert (tlsinv (p, 0, 1, 1), [NaN -Inf 0 Inf NaN], eps)
***** assert (tlsinv (p, 0, 1, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (tlsinv ([p(1:2) NaN p(4:5)], 0, 1, 1), [NaN -Inf NaN Inf NaN])
***** assert (class (tlsinv ([p, NaN], 0, 1, 1)), "double")
***** assert (class (tlsinv (single ([p, NaN]), 0, 1, 1)), "single")
***** assert (class (tlsinv ([p, NaN], single (0), 1, 1)), "single")
***** assert (class (tlsinv ([p, NaN], 0, single (1), 1)), "single")
***** assert (class (tlsinv ([p, NaN], 0, 1, single (1))), "single")
***** error<tlsinv: function called with too few input arguments.> tlsinv ()
***** error<tlsinv: function called with too few input arguments.> tlsinv (1)
***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2)
***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2, 3)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (3), ones (2), 1, 1)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (2), 1, ones (3), 1)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (2), 1, 1, ones (3))
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (i, 2, 3, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, i, 3, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, i, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, 3, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nakacdf.m
***** demo
 ## Plot various CDFs from the Nakagami distribution
 x = 0:0.01:3;
 p1 = nakacdf (x, 0.5, 1);
 p2 = nakacdf (x, 1, 1);
 p3 = nakacdf (x, 1, 2);
 p4 = nakacdf (x, 1, 3);
 p5 = nakacdf (x, 2, 1);
 p6 = nakacdf (x, 2, 2);
 p7 = nakacdf (x, 5, 1);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
       x, p5, "-k", x, p6, "-b", x, p7, "-c")
 grid on
 xlim ([0, 3])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "southeast")
 title ("Nakagami CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.63212055882855778, 0.98168436111126578, 1];
***** assert (nakacdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (nakacdf (x, 1, 1), y, eps)
***** assert (nakacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
***** assert (nakacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
***** assert (nakacdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (nakacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps("single"))
***** assert (nakacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps("single"))
***** assert (nakacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps("single"))
***** error<nakacdf: function called with too few input arguments.> nakacdf ()
***** error<nakacdf: function called with too few input arguments.> nakacdf (1)
***** error<nakacdf: function called with too few input arguments.> nakacdf (1, 2)
***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, "tail")
***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, 4)
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (3), ones (2), ones (2))
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (2), ones (3), ones (2))
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (2), ones (2), ones (3))
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (i, 2, 2)
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, i, 2)
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/triinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/triinv.m
***** demo
 ## Plot various iCDFs from the triangular distribution
 p = 0.001:0.001:0.999;
 x1 = triinv (p, 3, 6, 4);
 x2 = triinv (p, 1, 5, 2);
 x3 = triinv (p, 2, 9, 3);
 x4 = triinv (p, 2, 9, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([0, 10])
 legend ({"a = 3, b = 6, c = 4", "a = 1, b = 5, c = 2", ...
          "a = 2, b = 9, c = 3", "a = 2, b = 9, c = 5"}, ...
         "location", "northwest")
 title ("Triangular CDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-1, 0, 0.02, 0.5, 0.98, 1, 2];
 y = [NaN, 0, 0.1, 0.5, 0.9, 1, NaN] + 1;
***** assert (triinv (p, ones (1, 7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
***** assert (triinv (p, 1 * ones (1, 7), 1.5, 2), y, eps)
***** assert (triinv (p, 1, 1.5, 2 * ones (1, 7)), y, eps)
***** assert (triinv (p, 1, 1.5*ones (1,7), 2), y, eps)
***** assert (triinv (p, 1, 1.5, 2), y, eps)
***** assert (triinv (p, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv (p, 1, 1.5 * [1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv (p, 1, 1.5, 2 * [1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv ([p, NaN], 1, 1.5, 2), [y, NaN], eps)
***** assert (triinv (single ([p, NaN]), 1, 1.5, 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], single (1), 1.5, 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], 1, single (1.5), 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps('single'))
***** error<triinv: function called with too few input arguments.> triinv ()
***** error<triinv: function called with too few input arguments.> triinv (1)
***** error<triinv: function called with too few input arguments.> triinv (1, 2)
***** error<triinv: function called with too few input arguments.> triinv (1, 2, 3)
***** error<triinv: function called with too many inputs> ...
 triinv (1, 2, 3, 4, 5)
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (3), ones (2), ones(2), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (3), ones(2), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (2), ones(3), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (2), ones(2), ones(3))
***** error<triinv: P, A, B, and C must not be complex.> triinv (i, 2, 3, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, i, 3, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, i, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, 3, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/gpinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gpinv.m
***** demo
 ## Plot various iCDFs from the generalized Pareto distribution
 p = 0.001:0.001:0.999;
 x1 = gpinv (p, 1, 1, 0);
 x2 = gpinv (p, 5, 1, 0);
 x3 = gpinv (p, 20, 1, 0);
 x4 = gpinv (p, 1, 2, 0);
 x5 = gpinv (p, 5, 2, 0);
 x6 = gpinv (p, 20, 2, 0);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([0, 5])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "southeast")
 title ("Generalized Pareto iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y1, y2, y3
 p = [-1, 0, 1/2, 1, 2];
 y1 = [NaN, 0, 0.6931471805599453, Inf, NaN];
 y2 = [NaN, 0, 1, Inf, NaN];
 y3 = [NaN, 0, 1/2, 1, NaN];
***** assert (gpinv (p, zeros (1,5), ones (1,5), zeros (1,5)), y1)
***** assert (gpinv (p, 0, 1, zeros (1,5)), y1)
***** assert (gpinv (p, 0, ones (1,5), 0), y1)
***** assert (gpinv (p, zeros (1,5), 1, 0), y1)
***** assert (gpinv (p, 0, 1, 0), y1)
***** assert (gpinv (p, 0, 1, [0, 0, NaN, 0, 0]), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, 0, [1, 1, NaN, 1, 1], 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, [0, 0, NaN, 0, 0], 1, 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], 0, 1, 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, ones (1,5), ones (1,5), zeros (1,5)), y2)
***** assert (gpinv (p, 1, 1, zeros (1,5)), y2)
***** assert (gpinv (p, 1, ones (1,5), 0), y2)
***** assert (gpinv (p, ones (1,5), 1, 0), y2)
***** assert (gpinv (p, 1, 1, 0), y2)
***** assert (gpinv (p, 1, 1, [0, 0, NaN, 0, 0]), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, 1, [1, 1, NaN, 1, 1], 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, [1, 1, NaN, 1, 1], 1, 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], 1, 1, 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, -ones (1,5), ones (1,5), zeros (1,5)), y3)
***** assert (gpinv (p, -1, 1, zeros (1,5)), y3)
***** assert (gpinv (p, -1, ones (1,5), 0), y3)
***** assert (gpinv (p, -ones (1,5), 1, 0), y3)
***** assert (gpinv (p, -1, 1, 0), y3)
***** assert (gpinv (p, -1, 1, [0, 0, NaN, 0, 0]), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (p, -1, [1, 1, NaN, 1, 1], 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (p, -[1, 1, NaN, 1, 1], 1, 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], -1, 1, 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (single ([p, NaN]), 0, 1, 0), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], 0, 1, single (0)), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], 0, single (1), 0), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], single (0), 1, 0), single ([y1, NaN]))
***** assert (gpinv (single ([p, NaN]), 1, 1, 0), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], 1, 1, single (0)), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], 1, single (1), 0), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], single (1), 1, 0), single ([y2, NaN]))
***** assert (gpinv (single ([p, NaN]), -1, 1, 0), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], -1, 1, single (0)), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], -1, single (1), 0), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], single (-1), 1, 0), single ([y3, NaN]))
***** error<gpinv: function called with too few input arguments.> gpinv ()
***** error<gpinv: function called with too few input arguments.> gpinv (1)
***** error<gpinv: function called with too few input arguments.> gpinv (1, 2)
***** error<gpinv: function called with too few input arguments.> gpinv (1, 2, 3)
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (3), ones (2), ones(2), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (3), ones(2), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (2), ones(3), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (2), ones(2), ones(3))
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (i, 2, 3, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, i, 3, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, i, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, 3, i)
51 tests, 51 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncfcdf.m
***** demo
 ## Plot various CDFs from the noncentral F distribution
 x = 0:0.01:5;
 p1 = ncfcdf (x, 2, 5, 1);
 p2 = ncfcdf (x, 2, 5, 2);
 p3 = ncfcdf (x, 5, 10, 1);
 p4 = ncfcdf (x, 10, 20, 10);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
 grid on
 xlim ([0, 5])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "southeast")
 title ("Noncentral F CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral F CDF with LAMBDA = 10 to the F CDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 x = 0.01:0.1:10.01;
 p1 = ncfcdf (x, 5, 20, 10);
 p2 = fcdf (x, 5, 20);
 plot (x, p1, "-", x, p2, "-");
 grid on
 xlim ([0, 10])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "southeast")
 title ("Noncentral F vs F CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = ncfcdf (x, 10, 1, 3);
 assert (p([1:21]), zeros (1, 21), 1e-76);
 assert (p(22), 0.004530737275319753, 1e-14);
 assert (p(30), 0.255842099135669, 1e-14);
 assert (p(41), 0.4379890998457305, 1e-14);
***** test
 p = ncfcdf (12, 10, 3, 2);
 assert (p, 0.9582287900447416, 1e-14);
***** test
 p = ncfcdf (2, 3, 2, 1);
 assert (p, 0.5731985522994989, 1e-14);
***** test
 p = ncfcdf (2, 3, 2, 1, "upper");
 assert (p, 0.4268014477004823, 1e-14);
***** test
 p = ncfcdf ([3, 6], 3, 2, 5, "upper");
 assert (p, [0.530248523596927, 0.3350482341323044], 1e-14);
***** error<ncfcdf: function called with too few input arguments.> ncfcdf ()
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1)
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2)
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2, 3)
***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, "tail")
***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, 5)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (3), ones (2), ones (2), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (3), ones (2), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (2), ones (3), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (2), ones (2), ones (3))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (i, 2, 2, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, i, 2, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, i, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/chi2cdf.m
***** demo
 ## Plot various CDFs from the chi-squared distribution
 x = 0:0.01:8;
 p1 = chi2cdf (x, 1);
 p2 = chi2cdf (x, 2);
 p3 = chi2cdf (x, 3);
 p4 = chi2cdf (x, 4);
 p5 = chi2cdf (x, 6);
 p6 = chi2cdf (x, 9);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-y")
 grid on
 xlim ([0, 8])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "southeast")
 title ("Chi-squared CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p, u
 x = [-1, 0, 0.5, 1, 2];
 p = [0, (1 - exp (-x(2:end) / 2))];
 u = [1, 0, NaN, 0.3934693402873666, 0.6321205588285577];
***** assert (chi2cdf (x, 2 * ones (1,5)), p, eps)
***** assert (chi2cdf (x, 2), p, eps)
***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1]), [p(1), 1, NaN, p(4:5)], eps)
***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1], "upper"), ...
                        [p(1), 1, NaN, u(4:5)], eps)
***** assert (chi2cdf ([x(1:2), NaN, x(4:5)], 2), [p(1:2), NaN, p(4:5)], eps)
***** assert (chi2cdf ([x, NaN], 2), [p, NaN], eps)
***** assert (chi2cdf (single ([x, NaN]), 2), single ([p, NaN]), eps ("single"))
***** assert (chi2cdf ([x, NaN], single (2)), single ([p, NaN]), eps ("single"))
***** error<chi2cdf: function called with too few input arguments.> chi2cdf ()
***** error<chi2cdf: function called with too few input arguments.> chi2cdf (1)
***** error<chi2cdf: function called with too many inputs> chi2cdf (1, 2, 3, 4)
***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, 3)
***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, "uper")
***** error<chi2cdf: X and DF must be of common size or scalars.> ...
 chi2cdf (ones (3), ones (2))
***** error<chi2cdf: X and DF must be of common size or scalars.> ...
 chi2cdf (ones (2), ones (3))
***** error<chi2cdf: X and DF must not be complex.> chi2cdf (i, 2)
***** error<chi2cdf: X and DF must not be complex.> chi2cdf (2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/betacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/betacdf.m
***** demo
 ## Plot various CDFs from the Beta distribution
 x = 0:0.005:1;
 p1 = betacdf (x, 0.5, 0.5);
 p2 = betacdf (x, 5, 1);
 p3 = betacdf (x, 1, 3);
 p4 = betacdf (x, 2, 2);
 p5 = betacdf (x, 2, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "northwest")
 title ("Beta CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, x1, x2
 x = [-1 0 0.5 1 2];
 y = [0 0 0.75 1 1];
***** assert (betacdf (x, ones (1, 5), 2 * ones (1, 5)), y)
***** assert (betacdf (x, 1, 2 * ones (1, 5)), y)
***** assert (betacdf (x, ones (1, 5), 2), y)
***** assert (betacdf (x, [0 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
***** assert (betacdf (x, 1, 2 * [0 1 NaN 1 1]), [NaN 0 NaN 1 1])
***** assert (betacdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
 x1 = [0.1:0.2:0.9];
***** assert (betacdf (x1, 2, 2), [0.028, 0.216, 0.5, 0.784, 0.972], 1e-14);
***** assert (betacdf (x1, 2, 2, "upper"), 1 - [0.028, 0.216, 0.5, 0.784, 0.972],...
        1e-14);
 x2 = [1, 2, 3];
***** assert (betacdf (0.5, x2, x2), [0.5, 0.5, 0.5], 1e-14);
***** assert (betacdf ([x, NaN], 1, 2), [y, NaN])
***** assert (betacdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (betacdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (betacdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<betacdf: function called with too few input arguments.> betacdf ()
***** error<betacdf: function called with too few input arguments.> betacdf (1)
***** error<betacdf: function called with too few input arguments.> betacdf (1, 2)
***** error<betacdf: function called with too many inputs> betacdf (1, 2, 3, 4, 5)
***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, "tail")
***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, 4)
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (3), ones (2), ones (2))
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (2), ones (3), ones (2))
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (2), ones (2), ones (3))
***** error<betacdf: X, A, and B must not be complex.> betacdf (i, 2, 2)
***** error<betacdf: X, A, and B must not be complex.> betacdf (2, i, 2)
***** error<betacdf: X, A, and B must not be complex.> betacdf (2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gevrnd.m
***** assert(size (gevrnd (1,2,1)), [1, 1]);
***** assert(size (gevrnd (ones(2,1), 2, 1)), [2, 1]);
***** assert(size (gevrnd (ones(2,2), 2, 1)), [2, 2]);
***** assert(size (gevrnd (1, 2*ones(2,1), 1)), [2, 1]);
***** assert(size (gevrnd (1, 2*ones(2,2), 1)), [2, 2]);
***** assert(size (gevrnd (1, 2, 1, 3)), [3, 3]);
***** assert(size (gevrnd (1, 2, 1, [4 1])), [4, 1]);
***** assert(size (gevrnd (1, 2, 1, 4, 1)), [4, 1]);
***** assert (class (gevrnd (1,1,1)), "double")
***** assert (class (gevrnd (single (1),1,1)), "single")
***** assert (class (gevrnd (single ([1 1]),1,1)), "single")
***** assert (class (gevrnd (1,single (1),1)), "single")
***** assert (class (gevrnd (1,single ([1 1]),1)), "single")
***** assert (class (gevrnd (1,1,single (1))), "single")
***** assert (class (gevrnd (1,1,single ([1 1]))), "single")
***** error<gevrnd: function called with too few input arguments.> gevrnd ()
***** error<gevrnd: function called with too few input arguments.> gevrnd (1)
***** error<gevrnd: function called with too few input arguments.> gevrnd (1, 2)
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (3), ones (2), ones (2))
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (2), ones (3), ones (2))
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (2), ones (2), ones (3))
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (i, 2, 3)
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, i, 3)
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, 2, i)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, -1)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, 1.2)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, ones (2))
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, [2 -1 2])
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, [2 0 2.5])
***** error<gevrnd: dimensions must be non-negative integers.> ...
 gevrnd (1, 2, 3, 2, -1, 5)
***** error<gevrnd: dimensions must be non-negative integers.> ...
 gevrnd (1, 2, 3, 2, 1.5, 5)
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, 3)
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, [3, 2])
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, 3, 2)
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/dist_fun/copularnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/copularnd.m
***** test
 theta = 0.5;
 r = copularnd ("Gaussian", theta);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 df = 2;
 r = copularnd ("t", theta, df);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 r = copularnd ("Clayton", theta);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 n = 2;
 r = copularnd ("Clayton", theta, n);
 assert (size (r), [n, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = [1; 2];
 n = 2;
 d = 3;
 r = copularnd ("Clayton", theta, n, d);
 assert (size (r), [n, d]);
 assert (all ((r >= 0) & (r <= 1)));
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/burrpdf.m
***** demo
 ## Plot various PDFs from the Burr type XII distribution
 x = 0.001:0.001:3;
 y1 = burrpdf (x, 1, 1, 1);
 y2 = burrpdf (x, 1, 1, 2);
 y3 = burrpdf (x, 1, 1, 3);
 y4 = burrpdf (x, 1, 2, 1);
 y5 = burrpdf (x, 1, 3, 1);
 y6 = burrpdf (x, 1, 0.5, 2);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 ylim ([0, 2])
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "northeast")
 title ("Burr type XII PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 1, 1/4, 1/9, 0];
***** assert (burrpdf (x, ones(1,5), ones (1,5), ones (1,5)), y)
***** assert (burrpdf (x, 1, 1, 1), y)
***** assert (burrpdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf ([x, NaN], 1, 1, 1), [y, NaN])
***** assert (burrpdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], single (1), 1, 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], 1, single (1), 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]))
***** error<burrpdf: function called with too few input arguments.> burrpdf ()
***** error<burrpdf: function called with too few input arguments.> burrpdf (1)
***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2)
***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2, 3)
***** error<burrpdf: function called with too many inputs> ...
 burrpdf (1, 2, 3, 4, 5)
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (3), ones (2), ones(2), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (3), ones(2), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (2), ones(3), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (2), ones(2), ones(3))
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (i, 2, 3, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, i, 3, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, i, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, 3, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ricepdf.m
***** demo
 ## Plot various PDFs from the Rician distribution
 x = 0:0.01:8;
 y1 = ricepdf (x, 0, 1);
 y2 = ricepdf (x, 0.5, 1);
 y3 = ricepdf (x, 1, 1);
 y4 = ricepdf (x, 2, 1);
 y5 = ricepdf (x, 4, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
 grid on
 ylim ([0, 0.65])
 xlim ([0, 8])
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northeast")
 title ("Rician PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = [0 0 0.1073 0.1978 0.2846];
***** assert (ricepdf (x, ones (1, 5), 2 * ones (1, 5)), y, 1e-4)
***** assert (ricepdf (x, 1, 2 * ones (1, 5)), y, 1e-4)
***** assert (ricepdf (x, ones (1, 5), 2), y, 1e-4)
***** assert (ricepdf (x, [0 NaN 1 1 1], 2), [0 NaN y(3:5)], 1e-4)
***** assert (ricepdf (x, 1, 2 * [0 NaN 1 1 1]), [0 NaN y(3:5)], 1e-4)
***** assert (ricepdf ([x, NaN], 1, 2), [y, NaN], 1e-4)
***** assert (ricepdf (single ([x, NaN]), 1, 2), single ([y, NaN]), 1e-4)
***** assert (ricepdf ([x, NaN], single (1), 2), single ([y, NaN]), 1e-4)
***** assert (ricepdf ([x, NaN], 1, single (2)), single ([y, NaN]), 1e-4)
***** error<ricepdf: function called with too few input arguments.> ricepdf ()
***** error<ricepdf: function called with too few input arguments.> ricepdf (1)
***** error<ricepdf: function called with too few input arguments.> ricepdf (1,2)
***** error<ricepdf: function called with too many inputs> ricepdf (1,2,3,4)
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (3), ones (2), ones (2))
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (2), ones (3), ones (2))
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (2), ones (2), ones (3))
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (i, 2, 2)
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, i, 2)
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/bvtcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bvtcdf.m
***** test
 x = [1, 2];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
***** test
 x = [3, 2;2, 4;1, 5];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bisainv.m
***** demo
 ## Plot various iCDFs from the Birnbaum-Saunders distribution
 p = 0.001:0.001:0.999;
 x1 = bisainv (p, 1, 0.5);
 x2 = bisainv (p, 1, 1);
 x3 = bisainv (p, 1, 2);
 x4 = bisainv (p, 1, 5);
 x5 = bisainv (p, 1, 10);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 10])
 legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northwest")
 title ("Birnbaum-Saunders iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Plot various iCDFs from the Birnbaum-Saunders distribution
 p = 0.001:0.001:0.999;
 x1 = bisainv (p, 1, 0.3);
 x2 = bisainv (p, 2, 0.3);
 x3 = bisainv (p, 1, 0.5);
 x4 = bisainv (p, 3, 0.5);
 x5 = bisainv (p, 5, 0.5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 10])
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northwest")
 title ("Birnbaum-Saunders iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y, f
 f = @(p,b,c) (b * (c * norminv (p) + sqrt (4 + (c * norminv(p))^2))^2) / 4;
 p = [-1, 0, 1/4, 1/2, 1, 2];
 y = [NaN, 0, f(1/4, 1, 1), 1, Inf, NaN];
***** assert (bisainv (p, ones (1,6), ones (1,6)), y)
***** assert (bisainv (p, 1, ones (1,6)), y)
***** assert (bisainv (p, ones (1,6), 1), y)
***** assert (bisainv (p, 1, 1), y)
***** assert (bisainv (p, 1, [1, 1, 1, NaN, 1, 1]), [y(1:3), NaN, y(5:6)])
***** assert (bisainv (p, [1, 1, 1, NaN, 1, 1], 1), [y(1:3), NaN, y(5:6)])
***** assert (bisainv ([p, NaN], 1, 1), [y, NaN])
***** assert (bisainv (single ([p, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisainv ([p, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisainv ([p, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisainv: function called with too few input arguments.> bisainv ()
***** error<bisainv: function called with too few input arguments.> bisainv (1)
***** error<bisainv: function called with too few input arguments.> bisainv (1, 2)
***** error<bisainv: function called with too many inputs> bisainv (1, 2, 3, 4)
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (3), ones (2), ones(2))
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (2), ones (3), ones(2))
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (2), ones (2), ones(3))
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (i, 4, 3)
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, i, 3)
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/binocdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/binocdf.m
***** demo
 ## Plot various CDFs from the binomial distribution
 x = 0:40;
 p1 = binocdf (x, 20, 0.5);
 p2 = binocdf (x, 20, 0.7);
 p3 = binocdf (x, 40, 0.5);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "southeast")
 title ("Binomial CDF")
 xlabel ("values in x (number of successes)")
 ylabel ("probability")
***** shared x, p, p1
 x = [-1 0 1 2 3];
 p = [0 1/4 3/4 1 1];
 p1 = 1 - p;
***** assert (binocdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), p, eps)
***** assert (binocdf (x, 2, 0.5 * ones (1, 5)), p, eps)
***** assert (binocdf (x, 2 * ones (1, 5), 0.5), p, eps)
***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 1])
***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 1])
***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5), [p(1:2) NaN p(4:5)], eps)
***** assert (binocdf (99, 100, 0.1, "upper"), 1e-100, 1e-112);
***** assert (binocdf (x, 2 * ones (1, 5), 0.5*ones (1,5), "upper"), p1, eps)
***** assert (binocdf (x, 2, 0.5 * ones (1, 5), "upper"), p1, eps)
***** assert (binocdf (x, 2 * ones (1, 5), 0.5, "upper"), p1, eps)
***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5, "upper"), [1 NaN NaN NaN 0])
***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1], "upper"), [1 NaN NaN NaN 0])
***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5, "upper"), [p1(1:2) NaN p1(4:5)])
***** assert (binocdf ([x, NaN], 2, 0.5), [p, NaN], eps)
***** assert (binocdf (single ([x, NaN]), 2, 0.5), single ([p, NaN]))
***** assert (binocdf ([x, NaN], single (2), 0.5), single ([p, NaN]))
***** assert (binocdf ([x, NaN], 2, single (0.5)), single ([p, NaN]))
***** error<binocdf: function called with too few input arguments.> binocdf ()
***** error<binocdf: function called with too few input arguments.> binocdf (1)
***** error<binocdf: function called with too few input arguments.> binocdf (1, 2)
***** error<binocdf: function called with too many inputs> binocdf (1, 2, 3, 4, 5)
***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, "tail")
***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, 4)
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (3), ones (2), ones (2))
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (2), ones (3), ones (2))
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (2), ones (2), ones (3))
***** error<binocdf: X, N, and PS must not be complex.> binocdf (i, 2, 2)
***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, i, 2)
***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, 2, i)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/chi2pdf.m
***** demo
 ## Plot various PDFs from the chi-squared distribution
 x = 0:0.01:8;
 y1 = chi2pdf (x, 1);
 y2 = chi2pdf (x, 2);
 y3 = chi2pdf (x, 3);
 y4 = chi2pdf (x, 4);
 y5 = chi2pdf (x, 6);
 y6 = chi2pdf (x, 9);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-y")
 grid on
 xlim ([0, 8])
 ylim ([0, 0.5])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "northeast")
 title ("Chi-squared PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0, 1/2 * exp(-x(2:5)/2)];
***** assert (chi2pdf (x, 2*ones (1,5)), y)
***** assert (chi2pdf (x, 2), y)
***** assert (chi2pdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
***** assert (chi2pdf ([x, NaN], 2), [y, NaN])
***** assert (chi2pdf (single ([x, NaN]), 2), single ([y, NaN]))
***** assert (chi2pdf ([x, NaN], single (2)), single ([y, NaN]))
***** error<chi2pdf: function called with too few input arguments.> chi2pdf ()
***** error<chi2pdf: function called with too few input arguments.> chi2pdf (1)
***** error<chi2pdf: function called with too many inputs> chi2pdf (1,2,3)
***** error<chi2pdf: X and DF must be of common size or scalars.> ...
 chi2pdf (ones (3), ones (2))
***** error<chi2pdf: X and DF must be of common size or scalars.> ...
 chi2pdf (ones (2), ones (3))
***** error<chi2pdf: X and DF must not be complex.> chi2pdf (i, 2)
***** error<chi2pdf: X and DF must not be complex.> chi2pdf (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvtrnd.m
***** test
 rho = [1, 0.5; 0.5, 1];
 df = 3;
 n = 10;
 r = mvtrnd (rho, df, n);
 assert (size (r), [10, 2]);
***** test
 rho = [1, 0.5; 0.5, 1];
 df = [2; 3];
 n = 2;
 r = mvtrnd (rho, df, 2);
 assert (size (r), [2, 2]);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvnrnd.m
***** error<mvnrnd: too few input arguments.> mvnrnd ()
***** error<mvnrnd: too few input arguments.> mvnrnd ([2, 3, 4])
***** error<mvnrnd: wrong size of MU.> mvnrnd (ones (2, 2, 2), ones (1, 2, 3, 4))
***** error<mvnrnd: wrong size of SIGMA.> mvnrnd (ones (1, 3), ones (1, 2, 3, 4))
***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2])), [1, 3])
***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2], 10)), [10, 3])
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/laplacecdf.m
***** demo
 ## Plot various CDFs from the Laplace distribution
 x = -10:0.01:10;
 p1 = laplacecdf (x, 0, 1);
 p2 = laplacecdf (x, 0, 2);
 p3 = laplacecdf (x, 0, 4);
 p4 = laplacecdf (x, -5, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-10, 10])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "southeast")
 title ("Laplace CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, -log(2), 0, log(2), Inf];
 y = [0, 1/4, 1/2, 3/4, 1];
***** assert (laplacecdf ([x, NaN], 0, 1), [y, NaN])
***** assert (laplacecdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
***** assert (laplacecdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (laplacecdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (laplacecdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<laplacecdf: function called with too few input arguments.> laplacecdf ()
***** error<laplacecdf: function called with too few input arguments.> laplacecdf (1)
***** error<laplacecdf: function called with too few input arguments.> ...
 laplacecdf (1, 2)
***** error<laplacecdf: function called with too many inputs> ...
 laplacecdf (1, 2, 3, 4, 5)
***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, "tail")
***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, 4)
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (3), ones (2), ones (2))
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (2), ones (3), ones (2))
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (2), ones (2), ones (3))
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (i, 2, 2)
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, i, 2)
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gumbelcdf.m
***** demo
 ## Plot various CDFs from the Gumbel distribution
 x = -5:0.01:20;
 p1 = gumbelcdf (x, 0.5, 2);
 p2 = gumbelcdf (x, 1.0, 2);
 p3 = gumbelcdf (x, 1.5, 3);
 p4 = gumbelcdf (x, 3.0, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "southeast")
 title ("Gumbel CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = [0, 0.3679, 0.6922, 1];
***** assert (gumbelcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
***** assert (gumbelcdf (x, 1, ones (1,4)), y, 1e-4)
***** assert (gumbelcdf (x, ones (1,4), 1), y, 1e-4)
***** assert (gumbelcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
***** assert (gumbelcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
***** assert (gumbelcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
***** assert (gumbelcdf (x, "upper"), [1, 0.3078, 0.1266, 0], 1e-4)
***** assert (gumbelcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (gumbelcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
***** assert (gumbelcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
***** assert (gumbelcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf ()
***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf (1,2,3,4,5,6,7)
***** error<gumbelcdf: invalid argument for upper tail.> gumbelcdf (1, 2, 3, 4, "uper")
***** error<gumbelcdf: X, MU, and BETA must be of common size or scalars.> ...
 gumbelcdf (ones (3), ones (2), ones (2))
***** error<gumbelcdf: invalid size of covariance matrix.> gumbelcdf (2, 3, 4, [1, 2])
***** error<gumbelcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gumbelcdf (1, 2, 3)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (i, 2, 2)
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, i, 2)
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, 2, i)
***** error<gumbelcdf: bad covariance matrix.> ...
 [p, plo, pup] = gumbelcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/loglrnd.m
***** assert (size (loglrnd (1, 1)), [1 1])
***** assert (size (loglrnd (1, ones (2,1))), [2, 1])
***** assert (size (loglrnd (1, ones (2,2))), [2, 2])
***** assert (size (loglrnd (ones (2,1), 1)), [2, 1])
***** assert (size (loglrnd (ones (2,2), 1)), [2, 2])
***** assert (size (loglrnd (1, 1, 3)), [3, 3])
***** assert (size (loglrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (loglrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (loglrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (loglrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (loglrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (loglrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (loglrnd (1, 1)), "double")
***** assert (class (loglrnd (1, single (1))), "single")
***** assert (class (loglrnd (1, single ([1, 1]))), "single")
***** assert (class (loglrnd (single (1), 1)), "single")
***** assert (class (loglrnd (single ([1, 1]), 1)), "single")
***** error<loglrnd: function called with too few input arguments.> loglrnd ()
***** error<loglrnd: function called with too few input arguments.> loglrnd (1)
***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
 loglrnd (ones (3), ones (2))
***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
 loglrnd (ones (2), ones (3))
***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (i, 2, 3)
***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (1, i, 3)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, -1)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, 1.2)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, ones (2))
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, [2 -1 2])
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, [2 0 2.5])
***** error<loglrnd: dimensions must be non-negative integers.> ...
 loglrnd (1, 2, 2, -1, 5)
***** error<loglrnd: dimensions must be non-negative integers.> ...
 loglrnd (1, 2, 2, 1.5, 5)
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), 3)
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), [3, 2])
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/logirnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logirnd.m
***** assert (size (logirnd (1, 1)), [1 1])
***** assert (size (logirnd (1, ones (2,1))), [2, 1])
***** assert (size (logirnd (1, ones (2,2))), [2, 2])
***** assert (size (logirnd (ones (2,1), 1)), [2, 1])
***** assert (size (logirnd (ones (2,2), 1)), [2, 2])
***** assert (size (logirnd (1, 1, 3)), [3, 3])
***** assert (size (logirnd (1, 1, [4, 1])), [4, 1])
***** assert (size (logirnd (1, 1, 4, 1)), [4, 1])
***** assert (size (logirnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (logirnd (1, 1, 0, 1)), [0, 1])
***** assert (size (logirnd (1, 1, 1, 0)), [1, 0])
***** assert (size (logirnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (logirnd (1, 1)), "double")
***** assert (class (logirnd (1, single (1))), "single")
***** assert (class (logirnd (1, single ([1, 1]))), "single")
***** assert (class (logirnd (single (1), 1)), "single")
***** assert (class (logirnd (single ([1, 1]), 1)), "single")
***** error<logirnd: function called with too few input arguments.> logirnd ()
***** error<logirnd: function called with too few input arguments.> logirnd (1)
***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
 logirnd (ones (3), ones (2))
***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
 logirnd (ones (2), ones (3))
***** error<logirnd: MU and SIGMA must not be complex.> logirnd (i, 2, 3)
***** error<logirnd: MU and SIGMA must not be complex.> logirnd (1, i, 3)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, -1)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, 1.2)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, ones (2))
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, [2 -1 2])
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, [2 0 2.5])
***** error<logirnd: dimensions must be non-negative integers.> ...
 logirnd (1, 2, 2, -1, 5)
***** error<logirnd: dimensions must be non-negative integers.> ...
 logirnd (1, 2, 2, 1.5, 5)
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), 3)
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), [3, 2])
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unifcdf.m
***** demo
 ## Plot various CDFs from the continuous uniform distribution
 x = 0:0.1:10;
 p1 = unifcdf (x, 2, 5);
 p2 = unifcdf (x, 3, 9);
 plot (x, p1, "-b", x, p2, "-g")
 grid on
 xlim ([0, 10])
 ylim ([0, 1])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "southeast")
 title ("Continuous uniform CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 2] + 1;
 y = [0 0 0.5 1 1];
***** assert (unifcdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (unifcdf (x, ones (1,5), 2*ones (1,5), "upper"), 1 - y)
***** assert (unifcdf (x, 1, 2*ones (1,5)), y)
***** assert (unifcdf (x, 1, 2*ones (1,5), "upper"), 1 - y)
***** assert (unifcdf (x, ones (1,5), 2), y)
***** assert (unifcdf (x, ones (1,5), 2, "upper"), 1 - y)
***** assert (unifcdf (x, [2 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
***** assert (unifcdf (x, [2 1 NaN 1 1], 2, "upper"), 1 - [NaN 0 NaN 1 1])
***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1]), [NaN 0 NaN 1 1])
***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1], "upper"), 1 - [NaN 0 NaN 1 1])
***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2, "upper"), 1 - [y(1:2) NaN y(4:5)])
***** assert (unifcdf ([x, NaN], 1, 2), [y, NaN])
***** assert (unifcdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (unifcdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (unifcdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<unifcdf: function called with too few input arguments.> unifcdf ()
***** error<unifcdf: function called with too few input arguments.> unifcdf (1)
***** error<unifcdf: function called with too few input arguments.> unifcdf (1, 2)
***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, 4)
***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, "tail")
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (3), ones (2), ones (2))
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (2), ones (3), ones (2))
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (2), ones (2), ones (3))
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (i, 2, 2)
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, i, 2)
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, 2, i)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/vmcdf.m
***** demo
 ## Plot various CDFs from the von Mises distribution
 x1 = [-pi:0.1:pi];
 p1 = vmcdf (x1, 0, 0.5);
 p2 = vmcdf (x1, 0, 1);
 p3 = vmcdf (x1, 0, 2);
 p4 = vmcdf (x1, 0, 4);
 plot (x1, p1, "-r", x1, p2, "-g", x1, p3, "-b", x1, p4, "-c")
 grid on
 xlim ([-pi, pi])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p0, p1
 x = [-pi:pi/2:pi];
 p0 = [0, 0.10975, 0.5, 0.89025, 1];
 p1 = [0, 0.03752, 0.5, 0.99622, 1];
***** assert (vmcdf (x, 0, 1), p0, 1e-5)
***** assert (vmcdf (x, 0, 1, "upper"), 1 - p0, 1e-5)
***** assert (vmcdf (x, zeros (1,5), ones (1,5)), p0, 1e-5)
***** assert (vmcdf (x, zeros (1,5), ones (1,5), "upper"), 1 - p0, 1e-5)
***** assert (vmcdf (x, 0, [1 2 3 4 5]), p1, 1e-5)
***** assert (vmcdf (x, 0, [1 2 3 4 5], "upper"), 1 - p1, 1e-5)
***** assert (isa (vmcdf (single (pi), 0, 1), "single"), true)
***** assert (isa (vmcdf (pi, single (0), 1), "single"), true)
***** assert (isa (vmcdf (pi, 0, single (1)), "single"), true)
***** error<vmcdf: function called with too few input arguments.> vmcdf ()
***** error<vmcdf: function called with too few input arguments.> vmcdf (1)
***** error<vmcdf: function called with too few input arguments.> vmcdf (1, 2)
***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, "tail")
***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, 4)
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (3), ones (2), ones (2))
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (2), ones (3), ones (2))
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (2), ones (2), ones (3))
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (i, 2, 2)
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, i, 2)
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nakapdf.m
***** demo
 ## Plot various PDFs from the Nakagami distribution
 x = 0:0.01:3;
 y1 = nakapdf (x, 0.5, 1);
 y2 = nakapdf (x, 1, 1);
 y3 = nakapdf (x, 1, 2);
 y4 = nakapdf (x, 1, 3);
 y5 = nakapdf (x, 2, 1);
 y6 = nakapdf (x, 2, 2);
 y7 = nakapdf (x, 5, 1);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
       x, y5, "-k", x, y6, "-b", x, y7, "-c")
 grid on
 xlim ([0, 3])
 ylim ([0, 2])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "northeast")
 title ("Nakagami PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.73575888234288467, 0.073262555554936715, 0];
***** assert (nakapdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (nakapdf (x, 1, 1), y, eps)
***** assert (nakapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (nakapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (nakapdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (nakapdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (nakapdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (nakapdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<nakapdf: function called with too few input arguments.> nakapdf ()
***** error<nakapdf: function called with too few input arguments.> nakapdf (1)
***** error<nakapdf: function called with too few input arguments.> nakapdf (1, 2)
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (3), ones (2), ones(2))
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (2), ones (3), ones(2))
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (2), ones (2), ones(3))
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (i, 4, 3)
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, i, 3)
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, 4, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/wienrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wienrnd.m
***** error wienrnd (0)
***** error wienrnd (1, 3, -50)
***** error wienrnd (5, 0)
***** error wienrnd (0.4, 3, 5)
***** error wienrnd ([1 4], 3, 5)
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/tricdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tricdf.m
***** demo
 ## Plot various CDFs from the triangular distribution
 x = 0.001:0.001:10;
 p1 = tricdf (x, 3, 4, 6);
 p2 = tricdf (x, 1, 2, 5);
 p3 = tricdf (x, 2, 3, 9);
 p4 = tricdf (x, 2, 5, 9);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
          "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
         "location", "southeast")
 title ("Triangular CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
 y = [0, 0, 0.02, 0.5, 0.98, 1 1];
***** assert (tricdf (x, ones (1,7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2), y, eps)
***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2, "upper"), 1 - y, eps)
***** assert (tricdf (x, 1, 1.5, 2 * ones (1, 7)), y, eps)
***** assert (tricdf (x, 1, 1.5 * ones (1, 7), 2), y, eps)
***** assert (tricdf (x, 1, 1.5, 2), y, eps)
***** assert (tricdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf ([x, NaN], 1, 1.5, 2), [y, NaN], eps)
***** assert (tricdf (single ([x, NaN]), 1, 1.5, 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], single (1), 1.5, 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], 1, single (1.5), 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], 1, 1.5, single (2)), ...
 single ([y, NaN]), eps("single"))
***** error<tricdf: function called with too few input arguments.> tricdf ()
***** error<tricdf: function called with too few input arguments.> tricdf (1)
***** error<tricdf: function called with too few input arguments.> tricdf (1, 2)
***** error<tricdf: function called with too few input arguments.> tricdf (1, 2, 3)
***** error<tricdf: function called with too many inputs> ...
 tricdf (1, 2, 3, 4, 5, 6)
***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, "tail")
***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, 5)
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (3), ones (2), ones(2), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (3), ones(2), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (2), ones(3), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (2), ones(2), ones(3))
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (i, 2, 3, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, i, 3, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, i, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, 3, i)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nctrnd.m
***** assert (size (nctrnd (1, 1)), [1 1])
***** assert (size (nctrnd (1, ones (2,1))), [2, 1])
***** assert (size (nctrnd (1, ones (2,2))), [2, 2])
***** assert (size (nctrnd (ones (2,1), 1)), [2, 1])
***** assert (size (nctrnd (ones (2,2), 1)), [2, 2])
***** assert (size (nctrnd (1, 1, 3)), [3, 3])
***** assert (size (nctrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (nctrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (nctrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (nctrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (nctrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (nctrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (nctrnd (1, 1)), "double")
***** assert (class (nctrnd (1, single (1))), "single")
***** assert (class (nctrnd (1, single ([1, 1]))), "single")
***** assert (class (nctrnd (single (1), 1)), "single")
***** assert (class (nctrnd (single ([1, 1]), 1)), "single")
***** error<nctrnd: function called with too few input arguments.> nctrnd ()
***** error<nctrnd: function called with too few input arguments.> nctrnd (1)
***** error<nctrnd: DF and MU must be of common size or scalars.> ...
 nctrnd (ones (3), ones (2))
***** error<nctrnd: DF and MU must be of common size or scalars.> ...
 nctrnd (ones (2), ones (3))
***** error<nctrnd: DF and MU must not be complex.> nctrnd (i, 2)
***** error<nctrnd: DF and MU must not be complex.> nctrnd (1, i)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, -1)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, 1.2)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, ones (2))
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, [2 -1 2])
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, [2 0 2.5])
***** error<nctrnd: dimensions must be non-negative integers.> ...
 nctrnd (1, 2, 2, -1, 5)
***** error<nctrnd: dimensions must be non-negative integers.> ...
 nctrnd (1, 2, 2, 1.5, 5)
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), 3)
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), [3, 2])
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/gamrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gamrnd.m
***** assert (size (gamrnd (1, 1)), [1 1])
***** assert (size (gamrnd (1, ones (2,1))), [2, 1])
***** assert (size (gamrnd (1, ones (2,2))), [2, 2])
***** assert (size (gamrnd (ones (2,1), 1)), [2, 1])
***** assert (size (gamrnd (ones (2,2), 1)), [2, 2])
***** assert (size (gamrnd (1, 1, 3)), [3, 3])
***** assert (size (gamrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (gamrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (gamrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (gamrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (gamrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (gamrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (gamrnd (1, 1)), "double")
***** assert (class (gamrnd (1, single (1))), "single")
***** assert (class (gamrnd (1, single ([1, 1]))), "single")
***** assert (class (gamrnd (single (1), 1)), "single")
***** assert (class (gamrnd (single ([1, 1]), 1)), "single")
***** error<gamrnd: function called with too few input arguments.> gamrnd ()
***** error<gamrnd: function called with too few input arguments.> gamrnd (1)
***** error<gamrnd: A and B must be of common size or scalars.> ...
 gamrnd (ones (3), ones (2))
***** error<gamrnd: A and B must be of common size or scalars.> ...
 gamrnd (ones (2), ones (3))
***** error<gamrnd: A and B must not be complex.> gamrnd (i, 2, 3)
***** error<gamrnd: A and B must not be complex.> gamrnd (1, i, 3)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, -1)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, 1.2)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, ones (2))
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, [2 -1 2])
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, [2 0 2.5])
***** error<gamrnd: dimensions must be non-negative integers.> ...
 gamrnd (1, 2, 2, -1, 5)
***** error<gamrnd: dimensions must be non-negative integers.> ...
 gamrnd (1, 2, 2, 1.5, 5)
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), 3)
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), [3, 2])
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/iwishrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/iwishrnd.m
***** assert(size (iwishrnd (1,2,1)), [1, 1]);
***** assert(size (iwishrnd ([],2,1)), [1, 1]);
***** assert(size (iwishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
***** assert(size (iwishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
***** error iwishrnd ()
***** error iwishrnd (1)
***** error iwishrnd ([-3 1; 1 3],1)
***** error iwishrnd ([1; 1],1)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/bvncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bvncdf.m
***** demo
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 Z = reshape (p, 25, 25);
 surf (X1, X2, Z);
 title ("Bivariate Normal Distribution");
 ylabel "X1"
 xlabel "X2"
***** test
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 p_out = [0.00011878988774500, 0.00034404112322371, ...
          0.00087682502191813, 0.00195221905058185, ...
          0.00378235566873474, 0.00638175749734415, ...
          0.00943764224329656, 0.01239164888125426, ...
          0.01472750274376648, 0.01623228313374828]';
 assert (p([1:10]), p_out, 1e-16);
***** test
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 p_out = [0.8180695783608276, 0.8854485749482751, ...
          0.9308108777385832, 0.9579855743025508, ...
          0.9722897881414742, 0.9788150170059926, ...
          0.9813597788804785, 0.9821977956568989, ...
          0.9824283794464095, 0.9824809345614861]';
 assert (p([616:625]), p_out, 3e-16);
***** error bvncdf (randn (25,3), [], [1, 1; 1, 1]);
***** error bvncdf (randn (25,2), [], [1, 1; 1, 1]);
***** error bvncdf (randn (25,2), [], ones (3, 2));
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/tpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tpdf.m
***** demo
 ## Plot various PDFs from the Student's T distribution
 x = -5:0.01:5;
 y1 = tpdf (x, 1);
 y2 = tpdf (x, 2);
 y3 = tpdf (x, 5);
 y4 = tpdf (x, Inf);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.41])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "northeast")
 title ("Student's T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = rand (10,1);
 y = 1./(pi * (1 + x.^2));
 assert (tpdf (x, 1), y, 5*eps);
***** shared x, y
 x = [-Inf 0 0.5 1 Inf];
 y = 1./(pi * (1 + x.^2));
***** assert (tpdf (x, ones (1,5)), y, eps)
***** assert (tpdf (x, 1), y, eps)
***** assert (tpdf (x, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
***** assert (tpdf (x, Inf), normpdf (x))
***** assert (tpdf ([x, NaN], 1), [y, NaN], eps)
***** assert (tpdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (tpdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<tpdf: function called with too few input arguments.> tpdf ()
***** error<tpdf: function called with too few input arguments.> tpdf (1)
***** error<tpdf: X and DF must be of common size or scalars.> ...
 tpdf (ones (3), ones (2))
***** error<tpdf: X and DF must be of common size or scalars.> ...
 tpdf (ones (2), ones (3))
***** error<tpdf: X and DF must not be complex.> tpdf (i, 2)
***** error<tpdf: X and DF must not be complex.> tpdf (2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchypdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/cauchypdf.m
***** demo
 ## Plot various PDFs from the Cauchy distribution
 x = -5:0.01:5;
 y1 = cauchypdf (x, 0, 0.5);
 y2 = cauchypdf (x, 0, 1);
 y3 = cauchypdf (x, 0, 2);
 y4 = cauchypdf (x, -2, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.7])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northeast")
 title ("Cauchy PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = 1/pi * ( 2 ./ ((x-1).^2 + 2^2) );
***** assert (cauchypdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (cauchypdf (x, 1, 2*ones (1,5)), y)
***** assert (cauchypdf (x, ones (1,5), 2), y)
***** assert (cauchypdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
***** assert (cauchypdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
***** assert (cauchypdf ([x, NaN], 1, 2), [y, NaN])
***** assert (cauchypdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
***** assert (cauchypdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
***** assert (cauchypdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
***** test
 x = rand (10, 1);
 assert (cauchypdf (x, 0, 1), tpdf (x, 1), eps);
***** error<cauchypdf: function called with too few input arguments.> cauchypdf ()
***** error<cauchypdf: function called with too few input arguments.> cauchypdf (1)
***** error<cauchypdf: function called with too few input arguments.> ...
 cauchypdf (1, 2)
***** error<cauchypdf: function called with too many inputs> cauchypdf (1, 2, 3, 4)
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (3), ones (2), ones(2))
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (2), ones (3), ones(2))
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (2), ones (2), ones(3))
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (i, 4, 3)
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, i, 3)
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2inv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncx2inv.m
***** demo
 ## Plot various iCDFs from the noncentral chi-squared distribution
 p = 0.001:0.001:0.999;
 x1 = ncx2inv (p, 2, 1);
 x2 = ncx2inv (p, 2, 2);
 x3 = ncx2inv (p, 2, 3);
 x4 = ncx2inv (p, 4, 1);
 x5 = ncx2inv (p, 4, 2);
 x6 = ncx2inv (p, 4, 3);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", ...
       p, x4, "-m", p, x5, "-c", p, x6, "-y")
 grid on
 ylim ([0, 10])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northwest")
 title ("Noncentral chi-squared iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
 ## chi-squared CDF with the same number of degrees of freedom (4).

 p = 0.001:0.001:0.999;
 x1 = ncx2inv (p, 4, 2);
 x2 = chi2inv (p, 4);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([0, 10])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [0,0.3443,0.7226,1.1440,1.6220,2.1770,2.8436,3.6854,4.8447,6.7701,Inf];
 assert (ncx2inv ([0:0.1:1], 2, 1), x, 1e-4);
***** test
 x = [0,0.8295,1.6001,2.3708,3.1785,4.0598,5.0644,6.2765,7.8763,10.4199,Inf];
 assert (ncx2inv ([0:0.1:1], 2, 3), x, 1e-4);
***** test
 x = [0,0.5417,1.3483,2.1796,3.0516,4.0003,5.0777,6.3726,8.0748,10.7686,Inf];
 assert (ncx2inv ([0:0.1:1], 1, 4), x, 1e-4);
***** test
 x = [0.1808, 0.6456, 1.1842, 1.7650, 2.3760, 3.0105];
 assert (ncx2inv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
***** test
 x = [0.4887, 0.6699, 0.9012, 1.1842, 1.5164, 1.8927];
 assert (ncx2inv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
***** test
 x = [1.3941, 1.6824, 2.0103, 2.3760, NaN, 3.2087];
 assert (ncx2inv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
***** test
 assert (ncx2inv (0.996, 5, 8), 35.51298862765576, 2e-13);
***** error<ncx2inv: function called with too few input arguments.> ncx2inv ()
***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1)
***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1, 2)
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (3), ones (2), ones (2))
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (2), ones (3), ones (2))
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (2), ones (2), ones (3))
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (i, 2, 2)
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, i, 2)
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/plrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/plrnd.m
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (size (plrnd (x, Fx)), [1, 1])
***** assert (size (plrnd (x, Fx, 3)), [3, 3])
***** assert (size (plrnd (x, Fx, [4, 1])), [4, 1])
***** assert (size (plrnd (x, Fx, 4, 1)), [4, 1])
***** assert (size (plrnd (x, Fx, 4, 1, 5)), [4, 1, 5])
***** assert (size (plrnd (x, Fx, 0, 1)), [0, 1])
***** assert (size (plrnd (x, Fx, 1, 0)), [1, 0])
***** assert (size (plrnd (x, Fx, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (plrnd (x, Fx)), "double")
***** assert (class (plrnd (x, single (Fx))), "single")
***** assert (class (plrnd (single (x), Fx)), "single")
***** error<plrnd: function called with too few input arguments.> plrnd ()
***** error<plrnd: function called with too few input arguments.> plrnd (1)
***** error<plrnd: X and FX must be vectors of equal size.> ...
 plrnd ([0, 1, 2], [0, 1])
***** error<plrnd: X and FX must be at least two-elements long.> ...
 plrnd ([0], [1])
***** error<plrnd: FX must be bounded in the range> ...
 plrnd ([0, 1, 2], [0, 1, 1.5])
***** error<plrnd: FX must be bounded in the range> ...
 plrnd ([0, 1, 2], [0, i, 1])
***** error<plrnd: X and FX must not be complex.> ...
 plrnd ([0, i, 2], [0, 0.5, 1])
***** error<plrnd: X and FX must not be complex.> ...
 plrnd ([0, i, 2], [0, 0.5i, 1])
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, -1)
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, 1.2)
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, ones (2))
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, [2 -1 2])
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, [2 0 2.5])
***** error<plrnd: dimensions must be non-negative integers.> ...
 plrnd (x, Fx, 2, -1, 5)
***** error<plrnd: dimensions must be non-negative integers.> ...
 plrnd (x, Fx, 2, 1.5, 5)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/raylcdf.m
***** demo
 ## Plot various CDFs from the Rayleigh distribution
 x = 0:0.01:10;
 p1 = raylcdf (x, 0.5);
 p2 = raylcdf (x, 1);
 p3 = raylcdf (x, 2);
 p4 = raylcdf (x, 3);
 p5 = raylcdf (x, 4);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 legend ({"σ = 0.5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "southeast")
 title ("Rayleigh CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 p = raylcdf (x, sigma);
 expected_p = [0.0000, 0.0308, 0.0540, 0.0679, 0.0769, 0.0831];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = raylcdf (x, 0.5);
 expected_p = [0.0000, 0.3935, 0.8647, 0.9889, 0.9997, 1.0000];
 assert (p, expected_p, 0.001);
***** shared x, p
 x = [-1, 0, 1, 2, Inf];
 p = [0, 0, 0.39346934028737, 0.86466471676338, 1];
***** assert (raylcdf (x, 1), p, 1e-14)
***** assert (raylcdf (x, 1, "upper"), 1 - p, 1e-14)
***** error<raylcdf: function called with too few input arguments.> raylcdf ()
***** error<raylcdf: function called with too few input arguments.> raylcdf (1)
***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, "uper")
***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, 3)
***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
 raylcdf (ones (3), ones (2))
***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
 raylcdf (ones (2), ones (3))
***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (i, 2)
***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/geornd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/geornd.m
***** assert (size (geornd (0.5)), [1, 1])
***** assert (size (geornd (0.5*ones (2,1))), [2, 1])
***** assert (size (geornd (0.5*ones (2,2))), [2, 2])
***** assert (size (geornd (0.5, 3)), [3, 3])
***** assert (size (geornd (0.5, [4 1])), [4, 1])
***** assert (size (geornd (0.5, 4, 1)), [4, 1])
***** assert (class (geornd (0.5)), "double")
***** assert (class (geornd (single (0.5))), "single")
***** assert (class (geornd (single ([0.5 0.5]))), "single")
***** assert (class (geornd (single (0))), "single")
***** assert (class (geornd (single (1))), "single")
***** error<geornd: function called with too few input arguments.> geornd ()
***** error<geornd: PS must not be complex.> geornd (i)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, -1)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, 1.2)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, ones (2))
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, [2 -1 2])
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, [2 0 2.5])
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (ones (2), ones (2))
***** error<geornd: dimensions must be non-negative integers.> ...
 geornd (1, 2, -1, 5)
***** error<geornd: dimensions must be non-negative integers.> ...
 geornd (1, 2, 1.5, 5)
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 3)
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), [3, 2])
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 2, 3)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/norminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/norminv.m
***** demo
 ## Plot various iCDFs from the normal distribution
 p = 0.001:0.001:0.999;
 x1 = norminv (p, 0, 0.5);
 x2 = norminv (p, 0, 1);
 x3 = norminv (p, 0, 2);
 x4 = norminv (p, -2, 0.8);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 5])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northwest")
 title ("Normal iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
***** assert (norminv (p), probit (p))
***** assert (norminv (0.31254), probit (0.31254))
***** assert (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
***** assert (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
***** assert (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
***** assert (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))
***** error<norminv: function called with too few input arguments.> norminv ()
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (3), ones (2), ones (2))
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (2), ones (3), ones (2))
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (2), ones (2), ones (3))
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/invgpdf.m
***** demo
 ## Plot various PDFs from the inverse Gaussian distribution
 x = 0:0.001:3;
 y1 = invgpdf (x, 1, 0.2);
 y2 = invgpdf (x, 1, 1);
 y3 = invgpdf (x, 1, 3);
 y4 = invgpdf (x, 3, 0.2);
 y5 = invgpdf (x, 3, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-y")
 grid on
 xlim ([0, 3])
 ylim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northeast")
 title ("Inverse Gaussian PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0, 0, 0.8788, 0.3989, 0];
***** assert (invgpdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (invgpdf (x, 1, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 1, 1)), "single")
***** assert (class (hncdf ([x, NaN], 1, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (1), 1)), "single")
***** error<invgpdf: function called with too few input arguments.> invgpdf ()
***** error<invgpdf: function called with too few input arguments.> invgpdf (1)
***** error<invgpdf: function called with too few input arguments.> invgpdf (1, 2)
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (1, ones (2), ones (3))
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (ones (2), 1, ones (3))
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (ones (2), ones (3), 1)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (i, 2, 3)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, i, 3)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/jsupdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/jsupdf.m
***** error jsupdf ()
***** error jsupdf (1, 2, 3, 4)
***** error<jsupdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
 jsupdf (1, ones (2), ones (3))
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvncdf.m
***** demo
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 Z = reshape (p, 25, 25);
 surf (X1, X2, Z);
 title ("Bivariate Normal Distribution");
 ylabel "X1"
 xlabel "X2"
***** demo
 mu = [0, 0];
 Sigma = [0.25, 0.3; 0.3, 1];
 p = mvncdf ([0 0], [1 1], mu, Sigma);
 x1 = -3:.2:3;
 x2 = -3:.2:3;
 [X1, X2] = meshgrid (x1, x2);
 X = [X1(:), X2(:)];
 p = mvnpdf (X, mu, Sigma);
 p = reshape (p, length (x2), length (x1));
 contour (x1, x2, p, [0.0001, 0.001, 0.01, 0.05, 0.15, 0.25, 0.35]);
 xlabel ("x");
 ylabel ("p");
 title ("Probability over Rectangular Region");
 line ([0, 0, 1, 1, 0], [1, 0, 0, 1, 1], "Linestyle", "--", "Color", "k");
***** test
 fD = (-2:2)';
 X = repmat (fD, 1, 4);
 p = mvncdf (X);
 assert (p, [0; 0.0006; 0.0625; 0.5011; 0.9121], ones (5, 1) * 1e-4);
***** test
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 p_out = [0.00011878988774500, 0.00034404112322371, ...
          0.00087682502191813, 0.00195221905058185, ...
          0.00378235566873474, 0.00638175749734415, ...
          0.00943764224329656, 0.01239164888125426, ...
          0.01472750274376648, 0.01623228313374828]';
 assert (p([1:10]), p_out, 1e-16);
***** test
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 p_out = [0.8180695783608276, 0.8854485749482751, ...
          0.9308108777385832, 0.9579855743025508, ...
          0.9722897881414742, 0.9788150170059926, ...
          0.9813597788804785, 0.9821977956568989, ...
          0.9824283794464095, 0.9824809345614861]';
 assert (p([616:625]), p_out, 3e-16);
***** test
 mu = [0, 0];
 Sigma = [0.25, 0.3; 0.3, 1];
 [p, err] = mvncdf ([0, 0], [1, 1], mu, Sigma);
 assert (p, 0.2097424404755626, 1e-16);
 assert (err, 1e-08);
***** test
 x = [1 2];
 mu = [0.5 1.5];
 sigma = [1.0, 0.5; 0.5, 1.0];
 p = mvncdf (x, mu, sigma);
 assert (p, 0.546244443857090, 1e-15);
***** test
 x = [1 2];
 mu = [0.5 1.5];
 sigma = [1.0, 0.5; 0.5, 1.0];
 a = [-inf 0];
 p = mvncdf (a, x, mu, sigma);
 assert (p, 0.482672935215631, 1e-15);
***** error p = mvncdf (randn (25,26), [], eye (26));
***** error p = mvncdf (randn (25,8), [], eye (9));
***** error p = mvncdf (randn (25,4), randn (25,5), [], eye (4));
***** error p = mvncdf (randn (25,4), randn (25,4), [2, 3; 2, 3], eye (4));
***** error p = mvncdf (randn (25,4), randn (25,4), ones (1, 5), eye (4));
***** error p = mvncdf ([-inf, 0], [1, 2], [0.5, 1.5], [1.0, 0.5; 0.5, 1.0], option)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/jsucdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/jsucdf.m
***** error jsucdf ()
***** error jsucdf (1, 2, 3, 4)
***** error<jsucdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
 jsucdf (1, ones (2), ones (3))
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/raylinv.m
***** demo
 ## Plot various iCDFs from the Rayleigh distribution
 p = 0.001:0.001:0.999;
 x1 = raylinv (p, 0.5);
 x2 = raylinv (p, 1);
 x3 = raylinv (p, 2);
 x4 = raylinv (p, 3);
 x5 = raylinv (p, 4);
 plot (p, x1, "-b", p, x2, "g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
 grid on
 ylim ([0, 10])
 legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "northwest")
 title ("Rayleigh iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 p = 0:0.1:0.5;
 sigma = 1:6;
 x = raylinv (p, sigma);
 expected_x = [0.0000, 0.9181, 2.0041, 3.3784, 5.0538, 7.0645];
 assert (x, expected_x, 0.001);
***** test
 p = 0:0.1:0.5;
 x = raylinv (p, 0.5);
 expected_x = [0.0000, 0.2295, 0.3340, 0.4223, 0.5054, 0.5887];
 assert (x, expected_x, 0.001);
***** error<raylinv: function called with too few input arguments.> raylinv ()
***** error<raylinv: function called with too few input arguments.> raylinv (1)
***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
 raylinv (ones (3), ones (2))
***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
 raylinv (ones (2), ones (3))
***** error<raylinv: P and SIGMA must not be complex.> raylinv (i, 2)
***** error<raylinv: P and SIGMA must not be complex.> raylinv (2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/geoinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/geoinv.m
***** demo
 ## Plot various iCDFs from the geometric distribution
 p = 0.001:0.001:0.999;
 x1 = geoinv (p, 0.2);
 x2 = geoinv (p, 0.5);
 x3 = geoinv (p, 0.7);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 10])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northwest")
 title ("Geometric iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of failures)")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (geoinv (p, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (geoinv (p, 0.5), [NaN 0 1 Inf NaN])
***** assert (geoinv (p, 0.5*[1 -1 NaN 4 1]), [NaN NaN NaN NaN NaN])
***** assert (geoinv ([p(1:2) NaN p(4:5)], 0.5), [NaN 0 NaN Inf NaN])
***** assert (geoinv ([p, NaN], 0.5), [NaN 0 1 Inf NaN NaN])
***** assert (geoinv (single ([p, NaN]), 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (geoinv ([p, NaN], single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
***** error<geoinv: function called with too few input arguments.> geoinv ()
***** error<geoinv: function called with too few input arguments.> geoinv (1)
***** error<geoinv: P and PS must be of common size or scalars.> ...
 geoinv (ones (3), ones (2))
***** error<geoinv: P and PS must be of common size or scalars.> ...
 geoinv (ones (2), ones (3))
***** error<geoinv: P and PS must not be complex.> ...
 geoinv (i, 2)
***** error<geoinv: P and PS must not be complex.> ...
 geoinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/tinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tinv.m
***** demo
 ## Plot various iCDFs from the Student's T distribution
 p = 0.001:0.001:0.999;
 x1 = tinv (p, 1);
 x2 = tinv (p, 2);
 x3 = tinv (p, 5);
 x4 = tinv (p, Inf);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
 grid on
 xlim ([0, 1])
 ylim ([-5, 5])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "northwest")
 title ("Student's T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (tinv (p, ones (1,5)), [NaN -Inf 0 Inf NaN])
***** assert (tinv (p, 1), [NaN -Inf 0 Inf NaN], eps)
***** assert (tinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (tinv ([p(1:2) NaN p(4:5)], 1), [NaN -Inf NaN Inf NaN])
***** assert (tinv ([p, NaN], 1), [NaN -Inf 0 Inf NaN NaN], eps)
***** assert (tinv (single ([p, NaN]), 1), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
***** assert (tinv ([p, NaN], single (1)), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
***** error<tinv: function called with too few input arguments.> tinv ()
***** error<tinv: function called with too few input arguments.> tinv (1)
***** error<tinv: P and DF must be of common size or scalars.> ...
 tinv (ones (3), ones (2))
***** error<tinv: P and DF must be of common size or scalars.> ...
 tinv (ones (2), ones (3))
***** error<tinv: P and DF must not be complex.> tinv (i, 2)
***** error<tinv: P and DF must not be complex.> tinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2inv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/chi2inv.m
***** demo
 ## Plot various iCDFs from the chi-squared distribution
 p = 0.001:0.001:0.999;
 x1 = chi2inv (p, 1);
 x2 = chi2inv (p, 2);
 x3 = chi2inv (p, 3);
 x4 = chi2inv (p, 4);
 x5 = chi2inv (p, 6);
 x6 = chi2inv (p, 9);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-y")
 grid on
 ylim ([0, 8])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "northwest")
 title ("Chi-squared iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.3934693402873666 1 2];
***** assert (chi2inv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], 5*eps)
***** assert (chi2inv (p, 2), [NaN 0 1 Inf NaN], 5*eps)
***** assert (chi2inv (p, 2*[0 1 NaN 1 1]), [NaN 0 NaN Inf NaN], 5*eps)
***** assert (chi2inv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], 5*eps)
***** assert (chi2inv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], 5*eps)
***** assert (chi2inv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
***** assert (chi2inv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
***** error<chi2inv: function called with too few input arguments.> chi2inv ()
***** error<chi2inv: function called with too few input arguments.> chi2inv (1)
***** error<chi2inv: function called with too many inputs> chi2inv (1,2,3)
***** error<chi2inv: P and DF must be of common size or scalars.> ...
 chi2inv (ones (3), ones (2))
***** error<chi2inv: P and DF must be of common size or scalars.> ...
 chi2inv (ones (2), ones (3))
***** error<chi2inv: P and DF must not be complex.> chi2inv (i, 2)
***** error<chi2inv: P and DF must not be complex.> chi2inv (2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gumbelpdf.m
***** demo
 ## Plot various PDFs from the Extreme value distribution
 x = -5:0.001:20;
 y1 = gumbelpdf (x, 0.5, 2);
 y2 = gumbelpdf (x, 1.0, 2);
 y3 = gumbelpdf (x, 1.5, 3);
 y4 = gumbelpdf (x, 3.0, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 ylim ([0, 0.2])
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northeast")
 title ("Extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [0, 0.3679, 0.2547, 0.1182, 0.0474];
 y1 = [0, 0.1794, 0.3679, 0.2547, 0.1182];
***** assert (gumbelpdf (x), y0, 1e-4)
***** assert (gumbelpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
***** assert (gumbelpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
***** error<gumbelpdf: too few input arguments.> gumbelpdf ()
***** error<gumbelpdf: X, MU, and BETA must be of common size or scalars.> ...
 gumbelpdf (ones (3), ones (2), ones (2))
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (i, 2, 2)
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, i, 2)
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, 2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncx2pdf.m
***** demo
 ## Plot various PDFs from the noncentral chi-squared distribution
 x = 0:0.1:10;
 y1 = ncx2pdf (x, 2, 1);
 y2 = ncx2pdf (x, 2, 2);
 y3 = ncx2pdf (x, 2, 3);
 y4 = ncx2pdf (x, 4, 1);
 y5 = ncx2pdf (x, 4, 2);
 y6 = ncx2pdf (x, 4, 3);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", ...
       x, y4, "-m", x, y5, "-c", x, y6, "-y")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.32])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northeast")
 title ("Noncentral chi-squared PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral chi-squared PDF with LAMBDA = 2 to the
 ## chi-squared PDF with the same number of degrees of freedom (4).

 x = 0:0.1:10;
 y1 = ncx2pdf (x, 4, 2);
 y2 = chi2pdf (x, 4);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([0, 10])
 ylim ([0, 0.32])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df, d1
 x1 = [-Inf, 2, NaN, 4, Inf];
 df = [2, 0, -1, 1, 4];
 d1 = [1, NaN, 3, -1, 2];
***** assert (ncx2pdf (x1, df, d1), [0, NaN, NaN, NaN, 0]);
***** assert (ncx2pdf (x1, df, 1), [0, 0.07093996461786045, NaN, ...
                              0.06160064323277038, 0], 1e-14);
***** assert (ncx2pdf (x1, df, 3), [0, 0.1208364909271113, NaN, ...
                              0.09631299762429098, 0], 1e-14);
***** assert (ncx2pdf (x1, df, 2), [0, 0.1076346446244688, NaN, ...
                              0.08430464047296625, 0], 1e-14);
***** assert (ncx2pdf (x1, 2, d1), [0, NaN, NaN, NaN, 0]);
***** assert (ncx2pdf (2, df, d1), [0.1747201674611283, NaN, NaN, ...
                              NaN, 0.1076346446244688], 1e-14);
***** assert (ncx2pdf (4, df, d1), [0.09355987820265799, NaN, NaN, ...
                              NaN, 0.1192317192431485], 1e-14);
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf ()
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1)
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (3), ones (2), ones (2))
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (2), ones (3), ones (2))
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (2), ones (2), ones (3))
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (i, 2, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, i, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/expcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/expcdf.m
***** demo
 ## Plot various CDFs from the exponential distribution
 x = 0:0.01:5;
 p1 = expcdf (x, 2/3);
 p2 = expcdf (x, 1.0);
 p3 = expcdf (x, 2.0);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
 grid on
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "southeast")
 title ("Exponential CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p
 x = [-1 0 0.5 1 Inf];
 p = [0, 1 - exp(-x(2:end)/2)];
***** assert (expcdf (x, 2 * ones (1, 5)), p, 1e-16)
***** assert (expcdf (x, 2), p, 1e-16)
***** assert (expcdf (x, 2 * [1, 0, NaN, 1, 1]), [0, NaN, NaN, p(4:5)], 1e-16)
***** assert (expcdf ([x, NaN], 2), [p, NaN], 1e-16)
***** assert (expcdf (single ([x, NaN]), 2), single ([p, NaN]))
***** assert (expcdf ([x, NaN], single (2)), single ([p, NaN]))
***** test
 [p, plo, pup] = expcdf (1, 2, 3);
 assert (p, 0.39346934028737, 1e-14);
 assert (plo, 0.08751307220484, 1e-14);
 assert (pup, 0.93476821257933, 1e-14);
***** test
 [p, plo, pup] = expcdf (1, 2, 2, 0.1);
 assert (p, 0.39346934028737, 1e-14);
 assert (plo, 0.14466318041675, 1e-14);
 assert (pup, 0.79808291849140, 1e-14);
***** test
 [p, plo, pup] = expcdf (1, 2, 2, 0.1, "upper");
 assert (p, 0.60653065971263, 1e-14);
 assert (plo, 0.20191708150860, 1e-14);
 assert (pup, 0.85533681958325, 1e-14);
***** error<expcdf: invalid number of input arguments.> expcdf ()
***** error<expcdf: invalid number of input arguments.> expcdf (1, 2 ,3 ,4 ,5, 6)
***** error<expcdf: invalid argument for upper tail.> expcdf (1, 2, 3, 4, "uper")
***** error<expcdf: X and MU must be of common size or scalars.> ...
 expcdf (ones (3), ones (2))
***** error<expcdf: invalid size of variance, PCOV must be a scalar.> ...
 expcdf (2, 3, [1, 2])
***** error<expcdf: variance, PCOV, is required for confidence bounds.> ...
 [p, plo, pup] = expcdf (1, 2)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, 0)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, 1.22)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, "alpha", "upper")
***** error<expcdf: X and MU must not be complex.> expcdf (i, 2)
***** error<expcdf: X and MU must not be complex.> expcdf (2, i)
***** error<expcdf: variance, PCOV, cannot be negative.> ...
 [p, plo, pup] = expcdf (1, 2, -1, 0.04)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/manovacluster.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/manovacluster.m
***** demo
 load carbig
 X = [MPG Acceleration Weight Displacement];
 [d, p, stats] = manova1 (X, Origin);
 manovacluster (stats)
***** test
 hf = figure ("visible", "off");
 unwind_protect
   load carbig
   X = [MPG Acceleration Weight Displacement];
   [d, p, stats] = manova1 (X, Origin);
   manovacluster (stats);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error manovacluster (stats, "some");
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/combnk.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/combnk.m
***** demo
 c = combnk (1:5, 2);
 disp ("All pairs of integers between 1 and 5:");
 disp (c);
***** test
 c = combnk (1:3, 2);
 assert (c, [1, 2; 1, 3; 2, 3]);
***** test
 c = combnk (1:3, 6);
 assert (isempty (c));
***** test
 c = combnk ({1, 2, 3}, 2);
 assert (c, {1, 2; 1, 3; 2, 3});
***** test
 c = combnk ("hello", 2);
 assert (c, ["lo"; "lo"; "ll"; "eo"; "el"; "el"; "ho"; "hl"; "hl"; "he"]);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/nansum.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/nansum.m
***** assert (nansum ([2 4 NaN 7]), 13)
***** assert (nansum ([2 4 NaN Inf]), Inf)
***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN]), [8 13 9])
***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [4; 11; 15])
***** assert (nansum (single ([1 NaN 3; NaN 5 6; 7 8 NaN])), single ([8 13 9]))
***** assert (nansum (single ([1 NaN 3; NaN 5 6; 7 8 NaN]), "double"), [8 13 9])
***** assert (nansum (uint8 ([2 4 1 7])), 14)
***** assert (nansum (uint8 ([2 4 1 7]), "native"), uint8 (14))
***** assert (nansum (uint8 ([2 4 1 7])), 14)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/signrank.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/signrank.m
***** test
 load gradespaired.mat
 [p, h, stats] = signrank (gradespaired(:,1), ...
                           gradespaired(:,2), 'tail', 'left');
 assert (p, 0.0047, 1e-4);
 assert (h, true);
 assert (stats.zval, -2.5982, 1e-4);
 assert (stats.signedrank, 2017.5);
***** test
 load ('gradespaired.mat');
 [p, h, stats] = signrank (gradespaired(:,1), gradespaired(:,2), ...
                           'tail', 'left', 'method', 'exact');
 assert (p, 0.0045, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 2017.5);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33);
 assert (p, 0.0312, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 21);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right');
 assert (p, 0.0156, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 21);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right', ...
                           'alpha', 0.01, 'method', 'approximate');
 assert (p, 0.0180, 1e-4);
 assert (h, false);
 assert (stats.zval, 2.0966, 1e-4);
 assert (stats.signedrank, 21);
***** error <signrank: X must be a vector.> signrank (ones (2))
***** error <signrank: Y must be either a scalar of a vector.> ...
 signrank ([1, 2, 3, 4], ones (2))
***** error <signrank: X and Y vectors have different lengths.> ...
 signrank ([1, 2, 3, 4], [1, 2, 3])
***** error <signrank: optional arguments must be in pairs.> ...
 signrank ([1, 2, 3, 4], [], 'tail')
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', 1.2)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', 0)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', -0.05)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', "a")
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
***** error <signrank: 'tail' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'tail', 0.01)
***** error <signrank: 'tail' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'tail', {"both"})
***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
 signrank ([1, 2, 3, 4], [], 'tail', "some")
***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
 signrank ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
***** error <signrank: 'method' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'method', 0.01)
***** error <signrank: 'method' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'method', {"exact"})
***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
 signrank ([1, 2, 3, 4], [], 'method', "some")
***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
 signrank ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/fitcdiscr.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcdiscr.m
***** demo
 ## Train a linear discriminant classifier for Gamma = 0.5
 ## and plot the decision boundaries.

 load fisheriris
 idx = ! strcmp (species, "setosa");
 X = meas(idx,3:4);
 Y = cast (strcmpi (species(idx), "virginica"), "double");
 obj = fitcdiscr (X, Y, "Gamma", 0.5)
 x1 = [min(X(:,1)):0.03:max(X(:,1))];
 x2 = [min(X(:,2)):0.02:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);
 gidx = logical (str2num (cell2mat (pred)));

 figure
 scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel ("Petal length (cm)");
 ylabel ("Petal width (cm)");
 title ("Linear Discriminant Analysis Decision Boundary");
 legend ({"Versicolor Region", "Virginica Region", ...
         "Sampled Versicolor", "Sampled Virginica"}, ...
         "location", "northwest")
 axis tight
 hold off
***** test
 load fisheriris
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {meas, species, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
 assert (Mdl.ClassNames, unique (species))
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 assert (Mdl.Sigma, sigma, 1e-6)
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.LogDetSigma, -8.6884, 1e-4)
***** error<fitcdiscr: too few arguments.> fitcdiscr ()
***** error<fitcdiscr: too few arguments.> fitcdiscr (ones (4,1))
***** error<fitcdiscr: name-value arguments must be in pairs.>
 fitcdiscr (ones (4,2), ones (4, 1), "K")
***** error<fitcdiscr: number of rows in X and Y must be equal.>
 fitcdiscr (ones (4,2), ones (3, 1))
***** error<fitcdiscr: number of rows in X and Y must be equal.>
 fitcdiscr (ones (4,2), ones (3, 1), "K", 2)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/adtest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/adtest.m
***** error<Invalid call to adtest.  Correct usage is:> adtest ();
***** error<adtest: X must be a vector of real numbers.> adtest (ones (20,2));
***** error<adtest: X must be a vector of real numbers.> adtest ([1+i,0-3i]);
***** error<adtest: invalid distribution family in char string.> ...
 adtest (ones (20,1), "Distribution", "normal");
***** error<adtest: invalid distribution family in cell array.> ...
 adtest (rand (20,1), "Distribution", {"normal", 5, 3});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"norm", 5});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"exp", 5, 4});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"ev", 5});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"logn", 5, 3, 2});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"Weibull", 5});
***** error<adtest: invalid distribution option.> ...
 adtest (rand (20,1), "Distribution", 35);
***** error<adtest: invalid Name argument.> ...
 adtest (rand (20,1), "Name", "norm");
***** error<adtest: invalid Name argument.> ...
 adtest (rand (20,1), "Name", {"norm", 75, 10});
***** error<adtest: asymptotic option is not valid for the composite> ...
 adtest (rand (20,1), "Distribution", "norm", "Asymptotic", true);
***** error<adtest: asymptotic option is not valid for the composite> ...
 adtest (rand (20,1), "MCTol", 0.001, "Asymptotic", true);
***** error<adtest: asymptotic option is not valid for the Monte Carlo> ...
 adtest (rand (20,1), "Distribution", {"norm", 5, 3}, "MCTol", 0.001, ...
         "Asymptotic", true);
***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
 [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
                                 "Alpha", 0.000000001);
***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
 [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
                                 "Alpha", 0.999999999);
***** error<adtest: not enough data for composite testing.> ...
 adtest (10);
***** warning<adtest: out of range min p-value:> ...
 randn ("seed", 34);
 adtest (ones (20,1), "Alpha", 0.000001);
***** warning<adtest: alpha not within the lookup table.> ...
 randn ("seed", 34);
 adtest (normrnd(0,1,100,1), "Alpha", 0.99999);
***** warning<adtest: alpha not within the lookup table.> ...
 randn ("seed", 34);
 adtest (normrnd(0,1,100,1), "Alpha", 0.00001);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x);
 assert (h, false);
 assert (pval, 0.1854, 1e-4);
 assert (adstat, 0.5194, 1e-4);
 assert (cv, 0.7470, 1e-4);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x, "Distribution", "ev");
 assert (h, false);
 assert (pval, 0.071363, 1e-6);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x, "Distribution", {"norm", 75, 10});
 assert (h, false);
 assert (pval, 0.4687, 1e-4);
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/hmmviterbi.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hmmviterbi.m
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
             3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 vpath = hmmviterbi (sequence, transprob, outprob);
 expected = [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 assert (vpath, expected);
***** test
 sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
             "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 vpath = hmmviterbi (sequence, transprob, outprob, "symbols", symbols, ...
                                                   "statenames", statenames);
 expected = {"One", "One", "Two", "Two", "Two", "One", "One", "One", ...
             "One", "One", "One", "One", "One", "One", "One", "Two", ...
             "Two", "Two", "Two", "One", "One", "One", "One", "One", "One"};
 assert (vpath, expected);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/ismissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ismissing.m
***** assert (ismissing ([1,NaN,3]), [false,true,false])
***** assert (ismissing ('abcd f'), [false,false,false,false,true,false])
***** assert (ismissing ({'xxx','','xyz'}), [false,true,false])
***** assert (ismissing ({'x','','y'}), [false,true,false])
***** assert (ismissing ({'x','','y';'z','a',''}), logical([0,1,0;0,0,1]))
***** assert (ismissing ([1,2;NaN,2]), [false,false;true,false])
***** assert (ismissing ([1,2;NaN,2], 2), [false,true;false,true])
***** assert (ismissing ([1,2;NaN,2], [1 2]), [true,true;false,true])
***** assert (ismissing ([1,2;NaN,2], NaN), [false,false;true,false])
***** assert (ismissing (cat(3,magic(2),magic(2))), logical (zeros (2,2,2)))
***** assert (ismissing (cat(3,magic(2),[1 2;3 NaN])), logical (cat(3,[0,0;0,0],[0,0;0,1])))
***** assert (ismissing ([1 2; 3 4], [5 1; 2 0]), logical([1 1; 0 0]))
***** assert (ismissing (cat(3,'f oo','ba r')), logical(cat(3,[0 1 0 0],[0 0 1 0])))
***** assert (ismissing (cat(3,{'foo'},{''},{'bar'})), logical(cat(3,0,1,0)))
***** assert (ismissing (double (NaN)), true)
***** assert (ismissing (single (NaN)), true)
***** assert (ismissing (' '), true)
***** assert (ismissing ({''}), true)
***** assert (ismissing ({' '}), false)
***** assert (ismissing (double (eye(3)), single (1)), logical(eye(3)))
***** assert (ismissing (double (eye(3)), true), logical(eye(3)))
***** assert (ismissing (double (eye(3)), int32 (1)), logical(eye(3)))
***** assert (ismissing (single (eye(3)), true), logical(eye(3)))
***** assert (ismissing (single (eye(3)), double (1)), logical(eye(3)))
***** assert (ismissing (single(eye(3)), int32 (1)), logical(eye(3)))
***** assert (ismissing ({'123', '', 123}), [false false false])
***** assert (ismissing (logical ([1 0 1])), [false false false])
***** assert (ismissing (int32 ([1 2 3])), [false false false])
***** assert (ismissing (uint32 ([1 2 3])), [false false false])
***** assert (ismissing ({1, 2, 3}), [false false false])
***** assert (ismissing ([struct struct struct]), [false false false])
***** assert (ismissing (logical (eye(3)), true), logical(eye(3)))
***** assert (ismissing (logical (eye(3)), double (1)), logical(eye(3)))
***** assert (ismissing (logical (eye(3)), single (1)), logical(eye(3)))
***** assert (ismissing (logical (eye(3)), int32 (1)), logical(eye(3)))
***** assert (ismissing (int32 (eye(3)), int32 (1)), logical(eye(3)))
***** assert (ismissing (int32 (eye(3)), true), logical(eye(3)))
***** assert (ismissing (int32 (eye(3)), double (1)), logical(eye(3)))
***** assert (ismissing (int32 (eye(3)), single (1)), logical(eye(3)))
***** assert (ismissing ([]), logical([]))
***** assert (ismissing (''), logical([]))
***** assert (ismissing (ones (0,1)), logical(ones(0,1)))
***** assert (ismissing (ones (1,0)), logical(ones(1,0)))
***** assert (ismissing (ones (1,2,0)), logical(ones(1,2,0)))
***** error ismissing ()
***** error <'indicator' and 'A' must have the same> ismissing ([1 2; 3 4], "abc")
***** error <'indicator' and 'A' must have the same> ismissing ({"", "", ""}, 1)
***** error <'indicator' and 'A' must have the same> ismissing (1, struct)
***** error <indicators not supported for data type> ismissing (struct, 1)
49 tests, 49 passed, 0 known failure, 0 skipped
[inst/grpstats.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/grpstats.m
***** demo
 load carsmall;
 [m,p,g] = grpstats (Weight, Model_Year, {"mean", "predci", "gname"})
 n = length(m);
 errorbar((1:n)',m,p(:,2)-m);
 set (gca, "xtick", 1:n, "xticklabel", g);
 title ("95% prediction intervals for mean weight by year");
***** demo
 load carsmall;
 [m,p,g] = grpstats ([Acceleration,Weight/1000],Cylinders, ...
                     {"mean", "meanci", "gname"}, 0.05)
 [c,r] = size (m);
 errorbar((1:c)'.*ones(c,r),m,p(:,[(1:r)])-m);
 set (gca, "xtick", 1:c, "xticklabel", g);
 title ("95% prediction intervals for mean weight by year");
***** test
 load carsmall
 means = grpstats (Acceleration, Origin);
 assert (means, [14.4377; 18.0500; 15.8867; 16.3778; 16.6000; 15.5000], 0.001);
***** test
 load carsmall
 [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
 assert (grpMin, [8.0; 15.3; 13.9; 12.2; 15.7; 15.5]);
 assert (grpMax, [22.2; 21.9; 18.2; 24.6; 17.5; 15.5]);
***** test
 load carsmall
 [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
 assert (grp', {"USA", "France", "Japan", "Germany", "Sweden", "Italy"});
***** test
 load carsmall
 [m,p,g] = grpstats ([Acceleration,Weight/1000], Cylinders, ...
                     {"mean", "meanci", "gname"}, 0.05);
 assert (p(:,1), [11.17621760075134, 16.13845847655224, 16.16222663683362]', ...
                 [1e-14, 2e-14, 1e-14]');
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/vartest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/vartest2.m
***** error<vartest2: too few input arguments.> vartest2 ();
***** error<vartest2: too few input arguments.> vartest2 (ones (20,1));
***** error<vartest2: X and Y must be vectors or matrices or N-D arrays.> ...
 vartest2 (rand (20,1), 5);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 1.2);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", "some");
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", [0.05, 0.001]);
***** error<vartest2: invalid value for tail.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "tail", [0.05, 0.001]);
***** error<vartest2: invalid value for tail.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "tail", "some");
***** error<vartest2: invalid value for operating dimension.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "dim", 3);
***** error<vartest2: invalid value for operating dimension.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0.001, "dim", 3);
***** error<vartest2: invalid name for optional arguments.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "some", 3);
***** error<vartest2: optional arguments must be in name/value pairs.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "some");
***** test
 load carsmall
 [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76));
 assert (h, 0);
 assert (pval, 0.6288022362718455, 1e-13);
 assert (ci, [0.4139; 1.7193], 1e-4);
 assert (stat.fstat, 0.8384, 1e-4);
 assert (stat.df1, 30);
 assert (stat.df2, 33);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/slicesample.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/slicesample.m
***** demo
 ## Define function to sample
 d = 2;
 mu = [-1; 2];
 rand ("seed", 5)  # for reproducibility
 Sigma = rand (d);
 Sigma = (Sigma + Sigma');
 Sigma += eye (d)*abs (eigs (Sigma, 1, "sa")) * 1.1;
 pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));

 ## Inputs
 start = ones (1,2);
 nsamples = 500;
 K = 500;
 m = 10;
 rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
 [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "burnin", K, "thin", m, "width", [20, 30]);
 figure;
 hold on;
 plot (smpl(:,1), smpl(:,2), 'x');
 [x, y] = meshgrid (linspace (-6,4), linspace(-3,7));
 z = reshape (pdf ([x(:), y(:)]), size(x));
 mesh (x, y, z, "facecolor", "None");

 ## Using sample points to find the volume of half a sphere with radius of .5
 f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples^.5;
 trueerr = abs (2/3*pi*.25^(3/2)-int);
 fprintf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 fprintf ("Monte Carlo integral error estimate %f\n", errest);
 fprintf ("The actual error %f\n", trueerr);
 mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
***** demo
 ## Integrate truncated normal distribution to find normilization constant
 pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
 nsamples = 1e3;
 rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
 [smpl, accept] = slicesample (1, nsamples, "pdf", pdf, "thin", 4);
 f = @(x) exp (-.5 * x .^ 2) .* (x >= -2 & x <= 2);
 x = linspace (-3, 3, 1000);
 area (x, f(x));
 xlabel ("x");
 ylabel ("f(x)");
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ 0.5;
 trueerr = abs (erf (2 ^ 0.5) * 2 ^ 0.5 * pi ^ 0.5 - int);
 fprintf("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 fprintf("Monte Carlo integral error estimate %f\n", errest);
 fprintf("The actual error %f\n", trueerr);
***** test
 start = 0.5;
 nsamples = 1e3;
 pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
 [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "thin", 2, "burnin", 0, "width", 5);
 assert (mean (smpl, 1), 1, .15);
 assert (var (smpl, 1), 1, .25);
***** error slicesample ();
***** error slicesample (1);
***** error slicesample (1, 1);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/regression_ttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/regression_ttest.m
***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest ();
***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest (1);
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 NaN]', [2 3 4]');
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 Inf]', [2 3 4]');
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 3+i]', [2 3 4]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [2 3 NaN]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [2 3 Inf]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [3 4 3+i]');
***** error<regression_ttest: Y and X must be vectors of equal length.> ...
 regression_ttest ([1 2 3]', [3 4 4 5]');
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 1.2);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", "a");
***** error<regression_ttest: invalid Name argument.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "some", 0.05);
***** error<regression_ttest: invalid value for tail.>  ...
 regression_ttest ([1 2 3]', [2 3 4]', "tail", "val");
***** error<regression_ttest: invalid value for tail.>  ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/boxplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/boxplot.m
***** demo
 axis ([0, 3]);
 randn ("seed", 1);    # for reproducibility
 girls = randn (10, 1) * 5 + 140;
 randn ("seed", 2);    # for reproducibility
 boys = randn (13, 1) * 8 + 135;
 boxplot ({girls, boys});
 set (gca (), "xtick", [1 2], "xticklabel", {"girls", "boys"})
 title ("Grade 3 heights");
***** demo
 randn ("seed", 7);    # for reproducibility
 A = randn (10, 1) * 5 + 140;
 randn ("seed", 8);    # for reproducibility
 B = randn (25, 1) * 8 + 135;
 randn ("seed", 9);    # for reproducibility
 C = randn (20, 1) * 6 + 165;
 data = [A; B; C];
 groups = [(ones (10, 1)); (ones (25, 1) * 2); (ones (20, 1) * 3)];
 labels = {"Team A", "Team B", "Team C"};
 pos = [2, 1, 3];
 boxplot (data, groups, "Notch", "on", "Labels", labels, "Positions", pos, ...
          "OutlierTags", "on", "BoxStyle", "filled");
 title ("Example of Group splitting with paired vectors");
***** demo
 randn ("seed", 1);    # for reproducibility
 data = randn (100, 9);
 boxplot (data, "notch", "on", "boxstyle", "filled", ...
          "colors", "ygcwkmb", "whisker", 1.2);
 title ("Example of different colors specified with characters");
***** demo
 randn ("seed", 5);    # for reproducibility
 data = randn (100, 13);
 colors = [0.7 0.7 0.7; ...
           0.0 0.4 0.9; ...
           0.7 0.4 0.3; ...
           0.7 0.1 0.7; ...
           0.8 0.7 0.4; ...
           0.1 0.8 0.5; ...
           0.9 0.9 0.2];
 boxplot (data, "notch", "on", "boxstyle", "filled", ...
          "colors", colors, "whisker", 1.3, "boxwidth", "proportional");
 title ("Example of different colors specified as RGB values");
***** error <numerical array or cell array containing> boxplot ("a")
***** error <data cells must contain> boxplot ({[1 2 3], "a"})
***** error <grouping vector may only be passed> boxplot ([1 2 3], 1, {2, 3})
***** error <grouping vector must be numerical> boxplot ([1 2 3], {"a", "b"})
***** error <'Notch' input argument accepts> boxplot ([1:10], "notch", "any")
***** error <illegal Notch value> boxplot ([1:10], "notch", i)
***** error <illegal Notch value> boxplot ([1:10], "notch", {})
***** error <must be character> boxplot (1, "symbol", 1)
***** error <'Orientation' input argument accepts only> boxplot (1, "orientation", "diagonal")
***** error <illegal Orientation value> boxplot (1, "orientation", {})
***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", "a")
***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", [1 3])
***** error <'OutlierTags' input argument accepts only> boxplot (3, "OutlierTags", "maybe")
***** error <illegal OutlierTags value> boxplot (3, "OutlierTags", {})
***** error <'Sample_IDs' input argument accepts only> boxplot (1, "sample_IDs", 1)
***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", 2)
***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", "anything")
***** error <'Widths' input argument accepts only> boxplot (5, "widths", "a")
***** error <'Widths' input argument accepts only> boxplot (5, "widths", [1:4])
***** error <'Widths' input argument accepts only> boxplot (5, "widths", [])
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", "a")
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [1:4])
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [])
***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", 1)
***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", "garbage")
***** error <'Positions' input argument accepts only> boxplot (1, "positions", "aa")
***** error <'Labels' input argument accepts only> boxplot (3, "labels", [1 5])
***** error <'Colors' input argument accepts only> boxplot (1, "colors", {})
***** error <'Colors' input argument accepts only> boxplot (2, "colors", [1 2 3 4])
***** error <Sample_IDs must match the data> boxplot (randn (10, 3), 'Sample_IDs', {"a", "b"})
***** error <with the formalism> boxplot (rand (3, 3), [1 2])
***** test
 hf = figure ("visible", "off");
 unwind_protect
   [a, b] = boxplot (rand (10, 3));
   assert (size (a), [7, 3]);
   assert (numel (b.box), 3);
   assert (numel (b.whisker), 12);
   assert (numel (b.median), 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   [~, b] = boxplot (rand (10, 3), "BoxStyle", "filled", "colors", "ybc");
   assert (numel (b.box_fill), 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   hold on
   [a, b] = boxplot (rand (10, 3));
   assert (ishold, true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/kstest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/kstest.m
***** error kstest ()
***** error kstest (ones(2,4))
***** error kstest ([2,3,5,7,3+3i])
***** error kstest ([2,3,4,5,6],"tail")
***** error kstest ([2,3,4,5,6],"tail", "whatever")
***** error kstest ([2,3,4,5,6],"badoption", 0.51)
***** error kstest ([2,3,4,5,6],"tail", 0)
***** error kstest ([2,3,4,5,6],"alpha", 0)
***** error kstest ([2,3,4,5,6],"alpha", NaN)
***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal")
***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1])
***** test
 load examgrades
 [h, p] = kstest (grades(:,1));
 assert (h, true);
 assert (p, 7.58603305206105e-107, 1e-14);
***** test
 load stockreturns
 x = stocks(:,3);
 [h,p,k,c] = kstest (x, "Tail", "larger");
 assert (h, true);
 assert (p, 5.085438806199252e-05, 1e-14);
 assert (k, 0.2197, 1e-4);
 assert (c, 0.1207, 1e-4);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/ff2n.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ff2n.m
***** error ff2n ();
***** error ff2n (2, 5);
***** error ff2n (2.5);
***** error ff2n (0);
***** error ff2n (-3);
***** error ff2n (3+2i);
***** error ff2n (Inf);
***** error ff2n (NaN);
***** test
 A = ff2n (3);
 assert (A, fullfact (3));
***** test
 A = ff2n (8);
 assert (A, fullfact (8));
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/trimmean.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/trimmean.m
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 assert (trimmean (x, 10, "all"), 19.4722, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, [1, 2]);
 assert (out(1,1,1), 10.3889, 1e-4);
 assert (out(1,1,2), 29.6111, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 assert (trimmean (x, 10, "all"), 19.3824, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, 1);
 assert (out(:,:,1), [-17.6, 8, 13, 18]);
 assert (out(:,:,2), [23, 28, 33, 10.6]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, 1);
 assert (out(:,:,1), [-23, 8, 13, 18]);
 assert (out(:,:,2), [23, 28, 33, 3.75]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, 2);
 assert (out(:,:,1), [8.5; 9.5; -15.25; 11.5; 12.5]);
 assert (out(:,:,2), [28.5; -4.75; 30.5; 31.5; 32.5]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, 2);
 assert (out(:,:,1), [8.5; 9.5; -15.25; 14; 12.5]);
 assert (out(:,:,2), [28.5; -4.75; 28; 31.5; 32.5]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, [1, 2, 3]);
 assert (out, trimmean (x, 10, "all"));
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 2]);
 assert (out(1,1,1), 10.7647, 1e-4);
 assert (out(1,1,2), 29.1176, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 3]);
 assert (out, [2.5556, 18, 23, 11.6667], 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [2, 3]);
 assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 2, 3]);
 assert (out, trimmean (x, 10, "all"));
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [2, 3, 5]);
 assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
***** assert (trimmean (reshape (1:40, [5, 4, 2]), 10, 4), reshape(1:40, [5, 4, 2]))
***** assert (trimmean ([], 10), NaN)
***** assert (trimmean ([1;2;3;4;5], 10, 2), [1;2;3;4;5])
***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1)
***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1,2,3,4,5)
***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], -10)
***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], 100)
***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag")
***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag", 1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean ([1 2 3 4], 10, -1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean ([1 2 3 4], 10, "floor", -1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean (reshape (1:40, [5, 4, 2]), 10, [-1, 2])
***** error<trimmean: VECDIM must contain non-repeating positive integers.> ...
 trimmean (reshape (1:40, [5, 4, 2]), 10, [1, 2, 2])
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/probit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/probit.m
***** assert (probit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, Inf, NaN])
***** assert (probit ([0.2, 0.99]), norminv ([0.2, 0.99]))
***** error probit ()
***** error probit (1, 2)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/qrandn.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/qrandn.m
***** demo
 z = qrandn (-5, 5e6);
 [c x] = hist (z,linspace(-1.5,1.5,200),1);
 figure(1)
 plot(x,c,"r."); axis tight; axis([-1.5,1.5]);

 z = qrandn (-0.14286, 5e6);
 [c x] = hist (z,linspace(-2,2,200),1);
 figure(2)
 plot(x,c,"r."); axis tight; axis([-2,2]);

 z = qrandn (2.75, 5e6);
 [c x] = hist (z,linspace(-1e3,1e3,1e3),1);
 figure(3)
 semilogy(x,c,"r."); axis tight; axis([-100,100]);

 # ---------
 # Figures from the reference paper.
***** error<qrandn: the parameter q must be a scalar.> qrandn ([1 2], 1)
***** error<qrandn: the parameter q must be lower than 3.> qrandn (4, 1)
***** error<qrandn: the parameter q must be lower than 3.> qrandn (3, 1)
***** error qrandn (2.5, 1, 2, 3)
***** error qrandn (2.5)
***** test
 q = 1.5;
 s = [2, 3];
 z = qrandn (q, s);
 assert (isnumeric (z) && isequal (size (z), s));
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/mhsample.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/mhsample.m
***** demo
 ## Define function to sample
 d = 2;
 mu = [-1; 2];
 rand ("seed", 5)  # for reproducibility
 Sigma = rand (d);
 Sigma = (Sigma + Sigma');
 Sigma += eye (d) * abs (eigs (Sigma, 1, "sa")) * 1.1;
 pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
 ## Inputs
 start = ones (1, 2);
 nsamples = 500;
 sym = true;
 K = 500;
 m = 10;
 rand ("seed", 8)  # for reproducibility
 proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
 [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proprnd", proprnd, ...
                            "symmetric", sym, "burnin", K, "thin", m);
 figure;
 hold on;
 plot (smpl(:, 1), smpl(:, 2), 'x');
 [x, y] = meshgrid (linspace (-6, 4), linspace(-3, 7));
 z = reshape (pdf ([x(:), y(:)]), size(x));
 mesh (x, y, z, "facecolor", "None");
 ## Using sample points to find the volume of half a sphere with radius of .5
 f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ .5;
 trueerr = abs (2 / 3 * pi * .25 ^ (3 / 2) - int);
 printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 printf ("Monte Carlo integral error estimate %f\n", errest);
 printf ("The actual error %f\n", trueerr);
 mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
***** demo
 ## Integrate truncated normal distribution to find normilization constant
 pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
 nsamples = 1e3;
 rand ("seed", 5)  # for reproducibility
 proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
 [smpl, accept] = mhsample (1, nsamples, "pdf", pdf, "proprnd", proprnd, ...
                            "symmetric", true, "thin", 4);
 f = @(x) exp(-.5 * x .^ 2) .* (x >= -2 & x <= 2);
 x = linspace (-3, 3, 1000);
 area(x, f(x));
 xlabel ('x');
 ylabel ('f(x)');
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples^ .5;
 trueerr = abs (erf (2 ^ .5) * 2 ^ .5 * pi ^ .5 - int);
 printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 printf ("Monte Carlo integral error estimate %f\n", errest);
 printf ("The actual error %f\n", trueerr);
***** test
 nchain = 1e4;
 start = rand (nchain, 1);
 nsamples = 1e3;
 pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
 proppdf = @(x, y) 1/3;
 proprnd = @(x) 3 * (rand (size (x)) - .5) + x;
 [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proppdf", proppdf, ...
                            "proprnd", proprnd, "thin", 2, "nchain", nchain, ...
                            "burnin", 0);
 assert (mean (mean (smpl, 1), 3), 1, .01);
 assert (mean (var (smpl, 1), 3), 1, .01)
***** error mhsample ();
***** error mhsample (1);
***** error mhsample (1, 1);
***** error mhsample (1, 1, "pdf", @(x)x);
***** error mhsample (1, 1, "pdf", @(x)x, "proprnd", @(x)x+rand(size(x)));
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/pdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pdist.m
***** shared xy, t, eucl, x
 xy = [0 1; 0 2; 7 6; 5 6];
 t = 1e-3;
 eucl = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 x = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
***** assert (pdist (xy),                 [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, eucl),           [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "euclidean"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "seuclidean"),   [0.380 2.735 2.363 2.486 2.070 0.561], t);
***** assert (pdist (xy, "mahalanobis"),  [1.384 1.967 2.446 2.384 1.535 2.045], t);
***** assert (pdist (xy, "cityblock"),    [1.000 12.00 10.00 11.00 9.000 2.000], t);
***** assert (pdist (xy, "minkowski"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "minkowski", 3), [1.000 7.763 6.299 7.410 5.738 2.000], t);
***** assert (pdist (xy, "cosine"),       [0.000 0.349 0.231 0.349 0.231 0.013], t);
***** assert (pdist (xy, "correlation"),  [0.000 2.000 0.000 2.000 0.000 2.000], t);
***** assert (pdist (xy, "spearman"),     [0.000 2.000 0.000 2.000 0.000 2.000], t);
***** assert (pdist (xy, "hamming"),      [0.500 1.000 1.000 1.000 1.000 0.500], t);
***** assert (pdist (xy, "jaccard"),      [1.000 1.000 1.000 1.000 1.000 0.500], t);
***** assert (pdist (xy, "chebychev"),    [1.000 7.000 5.000 7.000 5.000 2.000], t);
***** assert (pdist (x), [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "euclidean"), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, eucl), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "squaredeuclidean"), [27, 108, 8, 27, 35, 116]);
***** assert (pdist (x, "seuclidean"), ...
        [1.8071, 3.6142, 0.9831, 1.8071, 1.8143, 3.4854], 1e-4);
***** warning<pdist: matrix is close to singular> ...
 pdist (x, "mahalanobis");
***** assert (pdist (x, "cityblock"), [9, 18, 4, 9, 9, 18]);
***** assert (pdist (x, "minkowski"), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "minkowski", 3), ...
        [4.3267, 8.6535, 2.5198, 4.3267, 5.3485, 9.2521], 1e-4);
***** assert (pdist (x, "cosine"), ...
        [0.0254, 0.0406, 0.2857, 0.0018, 0.1472, 0.1173], 1e-4);
***** assert (pdist (x, "correlation"), [0, 0, 2, 0, 2, 2], 1e-14);
***** assert (pdist (x, "spearman"), [0, 0, 2, 0, 2, 2], 1e-14);
***** assert (pdist (x, "hamming"), [1, 1, 2/3, 1, 1, 1]);
***** assert (pdist (x, "jaccard"), [1, 1, 2/3, 1, 1, 1]);
***** assert (pdist (x, "chebychev"), [3, 6, 2, 3, 5, 8]);
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/regress_gp.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/regress_gp.m
***** demo
 ## Linear fitting of 1D Data
 rand ("seed", 125);
 X = 2 * rand (5, 1) - 1;
 randn ("seed", 25);
 Y = 2 * X - 1 + 0.3 * randn (5, 1);

 ## Points for interpolation/extrapolation
 Xfit = linspace (-2, 2, 10)';

 ## Fit regression model
 [Yfit, Yint, m] = regress_gp (X, Y, Xfit);

 ## Plot fitted data
 plot (X, Y, "xk", Xfit, Yfit, "r-", Xfit, Yint, "b-");
 title ("Gaussian process regression with linear kernel");
***** demo
 ## Linear fitting of 2D Data
 rand ("seed", 135);
 X = 2 * rand (4, 2) - 1;
 randn ("seed", 35);
 Y = 2 * X(:,1) - 3 * X(:,2) - 1 + 1 * randn (4, 1);

 ## Mesh for interpolation/extrapolation
 [x1, x2] = meshgrid (linspace (-1, 1, 10));
 Xfit = [x1(:), x2(:)];

 ## Fit regression model
 [Ypred, Yint, Ysd] = regress_gp (X, Y, Xfit);
 Ypred = reshape (Ypred, 10, 10);
 YintU = reshape (Yint(:,1), 10, 10);
 YintL = reshape (Yint(:,2), 10, 10);

 ## Plot fitted data
 plot3 (X(:,1), X(:,2), Y, ".k", "markersize", 16);
 hold on;
 h = mesh (x1, x2, Ypred, zeros (10, 10));
 set (h, "facecolor", "none", "edgecolor", "yellow");
 h = mesh (x1, x2, YintU, ones (10, 10));
 set (h, "facecolor", "none", "edgecolor", "cyan");
 h = mesh (x1, x2, YintL, ones (10, 10));
 set (h, "facecolor", "none", "edgecolor", "cyan");
 hold off
 axis tight
 view (75, 25)
 title ("Gaussian process regression with linear kernel");
***** demo
 ## Projection over basis function with linear kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Define a prior covariance assuming that the sqrt component is not present
 Sp = 100 * eye (size (px, 2) + 1);
 Sp(2,2) = 1; # We don't believe the sqrt(abs(X)) is present

 ## Fit regression model
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, Sp);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("Linear kernel over basis function with prior covariance");
***** demo
 ## Projection over basis function with linear kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model without any assumption on prior covariance
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("Linear kernel over basis function without prior covariance");
***** demo
 ## Projection over basis function with rbf kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model with RBF kernel (standard parameters)
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf");

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("RBF kernel over basis function with standard parameters");
 text (-0.5, 4, "theta = 5\n g = 0.01");
***** demo
 ## Projection over basis function with rbf kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 10, 0.01);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 10\n g = 0.01");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.01);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.01");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.001);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.001");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.05);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.05");
***** demo
 ## RBF fitting on noiseless 1D Data
 x = [0:2*pi/7:2*pi]';
 y = 5 * sin (x);

 ## Predictive grid of 500 equally spaced locations
 xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';

 ## Fit regression model with RBF kernel
 [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");

 ## Plot fitted data
 r = mvnrnd (Yfit, diag (Ysd)', 50);
 plot (xi, r', "c-");
 hold on
 plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
 plot (x, y, ".k;Predictor points;", "markersize", 20)
 plot (xi, 5 * sin (xi), "-y;True Function;");
 xlim ([-0.5,2*pi+0.5]);
 ylim ([-10,10]);
 hold off
 title ("GP regression with RBF kernel on noiseless 1D data");
 text (0, -7, "theta = 5\n g = 0.01");
***** demo
 ## RBF fitting on noisy 1D Data
 x = [0:2*pi/7:2*pi]';
 x = [x; x];
 y = 5 * sin (x) + randn (size (x));

 ## Predictive grid of 500 equally spaced locations
 xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';

 ## Fit regression model with RBF kernel
 [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");

 ## Plot fitted data
 r = mvnrnd (Yfit, diag (Ysd)', 50);
 plot (xi, r', "c-");
 hold on
 plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
 plot (x, y, ".k;Predictor points;", "markersize", 20)
 plot (xi, 5 * sin (xi), "-y;True Function;");
 xlim ([-0.5,2*pi+0.5]);
 ylim ([-10,10]);
 hold off
 title ("GP regression with RBF kernel on noisy 1D data");
 text (0, -7, "theta = 5\n g = 0.01");
***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2))
***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2), ones (20, 1))
***** error<regress_gp: X must be a 2-D matrix.> ...
 regress_gp (ones (20, 2, 3), ones (20, 1), ones (20, 2))
***** error<regress_gp: Y must be a column vector.> ...
 regress_gp (ones (20, 2), ones (20, 2), ones (20, 2))
***** error<regress_gp: rows in X must equal the length of Y.> ...
 regress_gp (ones (20, 2), ones (15, 1), ones (20, 2))
***** error<regress_gp: X and XI must have the same number of columns.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (20, 3))
***** error<regress_gp: invalid 4th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), {[3]})
***** error<regress_gp: invalid 4th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "kernel")
***** error<regress_gp: theta must be a scalar when using RBF kernel.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", ones (4))
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", "value")
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", {5})
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), ones (3), 5)
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 5)
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, {5})
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, ones (2))
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, [1, 1])
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
***** error<regress_gp: invalid 7th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, "f")
***** error<regress_gp: invalid 7th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, [1, 1])
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/ridge.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ridge.m
***** demo
 ## Perform ridge regression for a range of ridge parameters and observe
 ## how the coefficient estimates change based on the acetylene dataset.

 load acetylene

 X = [x1, x2, x3];

 x1x2 = x1 .* x2;
 x1x3 = x1 .* x3;
 x2x3 = x2 .* x3;

 D = [x1, x2, x3, x1x2, x1x3, x2x3];

 k = 0:1e-5:5e-3;

 b = ridge (y, D, k);

 figure
 plot (k, b, "LineWidth", 2)
 ylim ([-100, 100])
 grid on
 xlabel ("Ridge Parameter")
 ylabel ("Standardized Coefficient")
 title ("Ridge Trace")
 legend ("x1", "x2", "x3", "x1x2", "x1x3", "x2x3")

***** demo

 load carbig
 X = [Acceleration Weight Displacement Horsepower];
 y = MPG;

 n = length(y);

 rand("seed",1); % For reproducibility

 c = cvpartition(n,'HoldOut',0.3);
 idxTrain = training(c,1);
 idxTest = ~idxTrain;

 idxTrain = training(c,1);
 idxTest = ~idxTrain;

 k = 5;
 b = ridge(y(idxTrain),X(idxTrain,:),k,0);

 % Predict MPG values for the test data using the model.
 yhat = b(1) + X(idxTest,:)*b(2:end);
 scatter(y(idxTest),yhat)

 hold on
 plot(y(idxTest),y(idxTest),"r")
 xlabel('Actual MPG')
 ylabel('Predicted MPG')
 hold off

***** test
 b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 1);
 assert (b, [0.5533; 0.5533], 1e-4);
***** test
 b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 2);
 assert (b, [0.4841; 0.4841], 1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0);
 assert (b,[10.2273;1.97128;-0.601818],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.0005);
 assert (b,[10.2233;1.9712;-0.6056],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.001);
 assert (b,[10.2194;1.9711;-0.6094],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.002);
 assert (b,[10.2116;1.9709;-0.6169],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.005);
 assert (b,[10.1882;1.9704;-0.6393],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.01);
 assert (b,[10.1497;1.9695;-0.6761],1e-4);
***** error<ridge: function called with too few input arguments.> ridge (1)
***** error<ridge: function called with too few input arguments.> ridge (1, 2)
***** error<ridge: Y must be a numeric column vector.> ridge (ones (3), ones (3), 2)
***** error<ridge: Y must be a numeric column vector.> ridge ([1, 2], ones (2), 2)
***** error<ridge: Y must be a numeric column vector.> ridge ([], ones (3), 2)
***** error<ridge: X must be a numeric matrix.> ridge (ones (5,1), [], 2)
***** error<ridge: Y and X must contain the same number of rows.> ...
 ridge ([1; 2; 3; 4; 5], ones (3), 3)
***** error<ridge: wrong value for SCALED argument.> ...
 ridge ([1; 2; 3], ones (3), 3, 2)
***** error<ridge: wrong value for SCALED argument.> ...
 ridge ([1; 2; 3], ones (3), 3, "some")
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/gscatter.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/gscatter.m
***** demo
 load fisheriris;
 X = meas(:,3:4);
 cidcs = kmeans (X, 3, "Replicates", 5);
 gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
 title ("Fisher's iris data");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 hf = figure ("visible", "off");
 unwind_protect
   load fisheriris;
   X = meas(:,3:4);
   cidcs = kmeans (X, 3, "Replicates", 5);
   gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
   title ("Fisher's iris data");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
warning: legend: 'best' not yet implemented for location specifier, using 'northeast' instead
***** error gscatter ();
***** error gscatter ([1]);
***** error gscatter ([1], [2]);
***** error <x must be a numeric vector> gscatter ('abc', [1 2 3], [1]);
***** error <x and y must have the same size> gscatter ([1 2 3], [1 2], [1]);
***** error <y must be a numeric vector> gscatter ([1 2 3], 'abc', [1]);
***** error <g must have the same size as x and y> gscatter ([1 2], [1 2], [1]);
***** error <invalid dolegend> gscatter ([1 2], [1 2], [1 2], 'rb', 'so', 12, 'xxx');
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/signtest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/signtest.m
***** test
 [pval, h, stats] = signtest ([-ones(1, 1000) 1], 0, "tail", "left");
 assert (pval, 1.091701889420221e-218, 1e-14);
 assert (h, 1);
 assert (stats.zval, -31.5437631079266, 1e-14);
***** test
 [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0);
 assert (pval, 0.6875000000000006, 1e-14);
 assert (h, 0);
 assert (stats.zval, NaN);
 assert (stats.sign, 4);
***** test
 [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0, "method", "approximate");
 assert (pval, 0.6830913983096086, 1e-14);
 assert (h, 0);
 assert (stats.zval, 0.4082482904638631, 1e-14);
 assert (stats.sign, 4);
***** error <signtest: X must be a vector.> signtest (ones (2))
***** error <signtest: Y must be either a scalar of a vector.> ...
 signtest ([1, 2, 3, 4], ones (2))
***** error <signtest: X and Y vectors have different lengths.> ...
 signtest ([1, 2, 3, 4], [1, 2, 3])
***** error <signtest: optional arguments must be in pairs.> ...
 signtest ([1, 2, 3, 4], [], 'tail')
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', 1.2)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', 0)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', -0.05)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', "a")
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
***** error <signtest: 'tail' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'tail', 0.01)
***** error <signtest: 'tail' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'tail', {"both"})
***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
 signtest ([1, 2, 3, 4], [], 'tail', "some")
***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
 signtest ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
***** error <signtest: 'method' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'method', 0.01)
***** error <signtest: 'method' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'method', {"exact"})
***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
 signtest ([1, 2, 3, 4], [], 'method', "some")
***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
 signtest ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_obj/tLocationScaleDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/tLocationScaleDistribution.m
***** shared pd, t
 pd = tLocationScaleDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.5, 0.8184, 0.9490, 0.9850, 0.9948, 0.9979], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7841, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.9030, 0.9490, 0.9850, 0.9948, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7841, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.9195, -0.2672, 0.2672, 0.9195, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1559, 2.3533, 2.6223, 3.0432, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2672, 0.2672, 0.9195, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3533, 2.6223, 3.0432, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4534, 1e-4);
***** assert (iqr (t), 0.7139, 1e-4);
***** assert (mean (pd), 0, eps);
***** assert (mean (t), 2.6099, 1e-4);
***** assert (median (pd), 0, eps);
***** assert (median (t), 2.4758, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3796, 0.2197, 0.0651, 0.0173, 0.0051, 0.0018], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.4209, 0.3775, 0.1119, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0.2197, 0.1245, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2910, 1e-4);
***** assert (std (t), 0.4989, 1e-4);
***** assert (var (pd), 1.6667, 1e-4);
***** assert (var (t), 0.2489, 1e-4);
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(i, 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(Inf, 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution([1, 2], 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution("a", 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(NaN, 1, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 0, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, -1, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, Inf, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, i, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, "a", 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, [1, 2], 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, NaN, 1)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, 0)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, -1)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, Inf)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, i)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, "a")
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, [1, 2])
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (tLocationScaleDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (tLocationScaleDistribution, 2, 3)
***** shared x
 x = tlsrnd (0, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), ...
          "parameter", {"mu", "sigma", "nu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "nu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (tLocationScaleDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (tLocationScaleDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (tLocationScaleDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (tLocationScaleDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (tLocationScaleDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (tLocationScaleDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (tLocationScaleDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (tLocationScaleDistribution)
***** error <truncate: missing input argument.> ...
 truncate (tLocationScaleDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (tLocationScaleDistribution, 4, 2)
***** shared pd
 pd = tLocationScaleDistribution (0, 1, 1);
 pd(2) = tLocationScaleDistribution (0, 1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/LogisticDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/LogisticDistribution.m
***** shared pd, t
 pd = LogisticDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.5, 0.7311, 0.8808, 0.9526, 0.9820, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7091, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8176, 0.8808, 0.9526, 0.9820], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7091, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.3863, -0.4055, 0.4055, 1.3863, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2088, 2.4599, 2.7789, 3.2252, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.4055, 0.4055, 1.3863, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4599, 2.7789, 3.2252, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.1972, 1e-4);
***** assert (iqr (t), 0.8286, 1e-4);
***** assert (mean (pd), 0, 1e-4);
***** assert (mean (t), 2.7193, 1e-4);
***** assert (median (pd), 0);
***** assert (median (t), 2.6085, 1e-4);
***** assert (pdf (pd, [0:5]), [0.25, 0.1966, 0.1050, 0.0452, 0.0177, 0.0066], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.0373, 0.4463, 0.1745, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1966, 0.1966, 0.1050, 0.0452, 0.0177, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0373, 0.4463, 0.1745, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.8138, 1e-4);
***** assert (std (t), 0.5320, 1e-4);
***** assert (var (pd), 3.2899, 1e-4);
***** assert (var (t), 0.2830, 1e-4);
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution(Inf, 1)
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution(i, 1)
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution("a", 1)
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution([1, 2], 1)
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution(NaN, 1)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, 0)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, -1)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, Inf)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, i)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, "a")
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, [1, 2])
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LogisticDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LogisticDistribution, 2, 3)
***** shared x
 x = logirnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LogisticDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LogisticDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LogisticDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LogisticDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LogisticDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LogisticDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LogisticDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LogisticDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LogisticDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LogisticDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LogisticDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LogisticDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LogisticDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LogisticDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LogisticDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LogisticDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LogisticDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LogisticDistribution, 4, 2)
***** shared pd
 pd = LogisticDistribution(1, 1);
 pd(2) = LogisticDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/NakagamiDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/NakagamiDistribution.m
***** shared pd, t
 pd = NakagamiDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.9817, 0.9999, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9933, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8946, 0.9817, 0.9999, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9933, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4724, 0.7147, 0.9572, 1.2686, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0550, 2.1239, 2.2173, 2.3684, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7147, 0.9572, 1.2686, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.1239, 2.2173, 2.3684, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.6411, 1e-4);
***** assert (iqr (t), 0.2502, 1e-4);
***** assert (mean (pd), 0.8862, 1e-4);
***** assert (mean (t), 2.2263, 1e-4);
***** assert (median (pd), 0.8326, 1e-4);
***** assert (median (t), 2.1664, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.7358, 0.0733, 0.0007, 0, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 4, 0.0404, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.7358, 0.0733, 0.0007, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 4, 0.0404, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.4633, 1e-4);
***** assert (std (t), 0.2083, 1e-4);
***** assert (var (pd), 0.2146, 1e-4);
***** assert (var (t), 0.0434, 1e-4);
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(Inf, 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(i, 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution("a", 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution([1, 2], 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(NaN, 1)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, 0)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, -1)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, Inf)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, i)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, "a")
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, [1, 2])
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NakagamiDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NakagamiDistribution, 2, 3)
***** shared x
 x = nakarnd (1, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NakagamiDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", {"mu", "omega", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "omega", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NakagamiDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NakagamiDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NakagamiDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NakagamiDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NakagamiDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NakagamiDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NakagamiDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NakagamiDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NakagamiDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NakagamiDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NakagamiDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NakagamiDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NakagamiDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NakagamiDistribution, 4, 2)
***** shared pd
 pd = NakagamiDistribution(1, 0.5);
 pd(2) = NakagamiDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/BirnbaumSaundersDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/BirnbaumSaundersDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Birnbaum-Saunders
 ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
 ## distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data

 pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
 randg ("seed", 21);
 data = random (pd, 5000, 1);
 pd = fitdist (data, "BirnbaumSaunders")
 plot (pd)
 msg = "Fitted Birnbaum-Saunders distribution with a = %0.2f and b = %0.2f";
 title (sprintf (msg, pd.beta, pd.gamma))
***** demo
 ## Plot the PDF of a Birnbaum-Saunders distribution, with parameters beta = 1
 ## and gamma = 0.5, truncated at [0, 2] intervals.  Generate 10000 random
 ## samples from this truncated distribution and superimpose a histogram with
 ## 100 bins scaled accordingly

 pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
 t = truncate (pd, 0, 2)
 randg ("seed", 21);
 data = random (t, 10000, 1);
 plot (t)
 title ("Birnbaum-Saunders distribution (a = 2, b = 4) truncated at [0.1, 0.8]")
 hold on
 hist (data, 100, 50)
 hold off
***** demo
 ## Generate a data set of 100 random samples from a Birnbaum-Saunders
 ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
 ## distribution to this data and plot its CDF superimposed over an empirical
 ## CDF of the data

 pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
 randg ("seed", 21);
 data = random (pd, 100, 1);
 pd = fitdist (data, "BirnbaumSaunders")
 plot (pd, "plottype", "cdf")
 title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
                 pd.beta, pd.gamma))
 legend ({"empirical CDF", "fitted CDF"}, "location", "east")
***** shared pd, t
 pd = BirnbaumSaundersDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7602, 0.8759, 0.9332, 0.9632], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6687, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6585, 0.7602, 0.8759, 0.9332, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.6687, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4411, 0.7767, 1.2875, 2.2673, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2293, 2.5073, 2.8567, 3.3210, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7767, 1.2875, 2.2673, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5073, 2.8567, 3.3210, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4236, 1e-4);
***** assert (iqr (t), 0.8968, 1e-4);
***** assert (mean (pd), 1.5, eps);
***** assert (mean (t), 2.7723, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.6711, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1648, 0.0788, 0.0405, 0.0216], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.9528, 0.4559, 0.2340, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2497, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.5, eps);
***** assert (std (t), 0.5528, 1e-4);
***** assert (var (pd), 2.25, eps);
***** assert (var (t), 0.3056, 1e-4);
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(0, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(Inf, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(i, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution("beta", 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution([1, 2], 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(NaN, 1)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, 0)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, -1)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, Inf)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, i)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, "beta")
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, [1, 2])
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BirnbaumSaundersDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BirnbaumSaundersDistribution, 2, 3)
***** shared x
 rand ("seed", 5);
 x = bisarnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "parameter", ...
          "beta", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), ...
          "parameter", {"beta", "gamma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"beta", "gamma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "beta", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BirnbaumSaundersDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BirnbaumSaundersDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BirnbaumSaundersDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BirnbaumSaundersDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BirnbaumSaundersDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BirnbaumSaundersDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BirnbaumSaundersDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BirnbaumSaundersDistribution, 4, 2)
***** shared pd
 pd = BirnbaumSaundersDistribution(1, 1);
 pd(2) = BirnbaumSaundersDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/NormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/NormalDistribution.m
***** shared pd, t
 pd = NormalDistribution;
 t = truncate (pd, -2, 2);
***** assert (cdf (pd, [0:5]), [0.5, 0.8413, 0.9772, 0.9987, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0.5, 0.8576, 1, 1, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9332, 0.9772, 0.9987, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0.9538, 1, 1, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.8416, -0.2533, 0.2533, 0.8416, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [-2, -0.7938, -0.2416, 0.2416, 0.7938, 2], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2533, 0.2533, 0.8416, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2416, 0.2416, 0.7938, 2, NaN], 1e-4);
***** assert (iqr (pd), 1.3490, 1e-4);
***** assert (iqr (t), 1.2782, 1e-4);
***** assert (mean (pd), 0);
***** assert (mean (t), 0, 3e-16);
***** assert (median (pd), 0);
***** assert (median (t), 0, 3e-16);
***** assert (pdf (pd, [0:5]), [0.3989, 0.2420, 0.0540, 0.0044, 0.0001, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0.4180, 0.2535, 0.0566, 0, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2420, 0.2420, 0.0540, 0.0044, 0.0001, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0.2535, 0.2535, 0.0566, 0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < -2), false);
***** assert (any (random (t, 1000, 1) > 2), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.8796, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.7737, 1e-4);
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(Inf, 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(i, 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution("a", 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution([1, 2], 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(NaN, 1)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, 0)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, -1)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, Inf)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, i)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, "a")
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, [1, 2])
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NormalDistribution, 2, 3)
***** shared x
 x = normrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NormalDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NormalDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NormalDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NormalDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NormalDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NormalDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NormalDistribution, 4, 2)
***** shared pd
 pd = NormalDistribution(1, 1);
 pd(2) = NormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/BetaDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/BetaDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Beta distribution with
 ## parameters a = 2 and b = 4.  Fit a Beta distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of the data

 pd = makedist ("Beta", "a", 2, "b", 4)
 randg ("seed", 21);
 data = random (pd, 5000, 1);
 pd = fitdist (data, "Beta")
 plot (pd)
 title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
                 pd.a, pd.b))
***** demo
 ## Plot the PDF of a Beta distribution, with parameters a = 2 and b = 4,
 ## truncated at [0.1, 0.8] intervals.  Generate 10000 random samples from
 ## this truncated distribution and superimpose a histogram with 100 bins
 ## scaled accordingly

 pd = makedist ("Beta", "a", 2, "b", 4)
 t = truncate (pd, 0.1, 0.8)
 randg ("seed", 21);
 data = random (t, 10000, 1);
 plot (t)
 title ("Beta distribution (a = 2, b = 4) truncated at [0.1, 0.8]")
 hold on
 hist (data, 100, 140)
 hold off
***** demo
 ## Generate a data set of 100 random samples from a Beta distribution with
 ## parameters a = 2 and b = 4.  Fit a Beta distribution to this data and plot
 ## its CDF superimposed over an empirical CDF of the data

 pd = makedist ("Beta", "a", 2, "b", 4)
 randg ("seed", 21);
 data = random (pd, 100, 1);
 pd = fitdist (data, "Beta")
 plot (pd, "plottype", "cdf")
 title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
                 pd.a, pd.b))
 legend ({"empirical CDF", "fitted CDF"}, "location", "east")
***** demo
 ## Generate a data set of 200 random samples from a Beta distribution with
 ## parameters a = 2 and b = 4.  Display a probability plot for the Beta
 ## distribution fit to the data.

 pd = makedist ("Beta", "a", 2, "b", 4)
 randg ("seed", 21);
 data = random (pd, 200, 1);
 pd = fitdist (data, "Beta")
 plot (pd, "plottype", "probability")
 title (sprintf ("Probability plot of a fitted Beta distribution with a = %0.2f and b = %0.2f", ...
                 pd.a, pd.b))
 legend ({"empirical CDF", "fitted CDF"}, "location", "southeast")
***** shared pd, t
 pd = BetaDistribution;
 t = truncate (pd, 0.2, 0.8);
***** assert (cdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (cdf (t, [0:0.2:1]), [0, 0, 0.3333, 0.6667, 1, 1], 1e-4);
***** assert (cdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (cdf (t, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [0.2, 0.32, 0.44, 0.56, 0.68, 0.8], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.4, 0.6, 0.8, 1, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 0.44, 0.56, 0.68, 0.8, NaN], 1e-4);
***** assert (iqr (pd), 0.5, 1e-4);
***** assert (iqr (t), 0.3, 1e-4);
***** assert (mean (pd), 0.5);
***** assert (mean (t), 0.5, 1e-6);
***** assert (median (pd), 0.5);
***** assert (median (t), 0.5, 1e-6);
***** assert (pdf (pd, [0:0.2:1]), [1, 1, 1, 1, 1, 1], 1e-4);
***** assert (pdf (t, [0:0.2:1]), [0, 1.6667, 1.6667, 1.6667, 1.6667, 0], 1e-4);
***** assert (pdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (pdf (t, [-1, 1, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 0.2), false);
***** assert (any (random (t, 1000, 1) > 0.8), false);
***** assert (std (pd), 0.2887, 1e-4);
***** assert (std (t), 0.1732, 1e-4);
***** assert (var (pd), 0.0833, 1e-4);
***** assert (var (t), 0.0300, 1e-4);
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(0, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(Inf, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(i, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution("a", 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution([1, 2], 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(NaN, 1)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, 0)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, -1)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, Inf)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, i)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, "a")
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, [1, 2])
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BetaDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BetaDistribution, 2, 3)
***** shared x
 randg ("seed", 1);
 x = betarnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BetaDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BetaDistribution.fit (x), "parameter", {"a", "b", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"a", "b", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BetaDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BetaDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BetaDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BetaDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BetaDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BetaDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BetaDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BetaDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BetaDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BetaDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BetaDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BetaDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BetaDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BetaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BetaDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BetaDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BetaDistribution, 4, 2)
***** shared pd
 pd = BetaDistribution(1, 1);
 pd(2) = BetaDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/RayleighDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/RayleighDistribution.m
***** shared pd, t
 pd = RayleighDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.3935, 0.8647, 0.9889, 0.9997, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9202, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6753, 0.8647, 0.9889, 0.9997, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.9202, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.6680, 1.0108, 1.3537, 1.7941, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1083, 2.2402, 2.4135, 2.6831, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.0108, 1.3537, 1.7941, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2402, 2.4135, 2.6831, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.9066, 1e-4);
***** assert (iqr (t), 0.4609, 1e-4);
***** assert (mean (pd), 1.2533, 1e-4);
***** assert (mean (t), 2.4169, 1e-4);
***** assert (median (pd), 1.1774, 1e-4);
***** assert (median (t), 2.3198, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.6065, 0.2707, 0.0333, 0.0013, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 2.0050, 0.2469, 0.0099, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4870, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.6551, 1e-4);
***** assert (std (t), 0.3591, 1e-4);
***** assert (var (pd), 0.4292, 1e-4);
***** assert (var (t), 0.1290, 1e-4);
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(0)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(-1)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(Inf)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(i)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution("a")
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution([1, 2])
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RayleighDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RayleighDistribution, 2, 3)
***** shared x
 x = raylrnd (1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (RayleighDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RayleighDistribution.fit (x), "parameter", {"sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (RayleighDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (RayleighDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RayleighDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (RayleighDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RayleighDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RayleighDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RayleighDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (RayleighDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (RayleighDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (RayleighDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (RayleighDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (RayleighDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RayleighDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RayleighDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (RayleighDistribution)
***** error <truncate: missing input argument.> ...
 truncate (RayleighDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (RayleighDistribution, 4, 2)
***** shared pd
 pd = RayleighDistribution(1);
 pd(2) = RayleighDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
90 tests, 90 passed, 0 known failure, 0 skipped
[inst/dist_obj/WeibullDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/WeibullDistribution.m
***** shared pd, t
 pd = WeibullDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.7769, 0.8647, 0.9502, 0.9817, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7311, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1, 1e-14);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2231, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1, 1e-14);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1, 1e-14);
***** assert (var (t), 0.2759, 1e-4);
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(0, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(-1, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(Inf, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(i, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution("a", 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution([1, 2], 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(NaN, 1)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, 0)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, -1)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, Inf)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, i)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, "a")
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, [1, 2])
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (WeibullDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (WeibullDistribution, 2, 3)
***** shared x
 x = wblrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (WeibullDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "parameter", "k", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (WeibullDistribution.fit (x), "parameter", {"lambda", "k", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"lambda", "k", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (WeibullDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "k", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (WeibullDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (WeibullDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (WeibullDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (WeibullDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (WeibullDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (WeibullDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (WeibullDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (WeibullDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (WeibullDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (WeibullDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (WeibullDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (WeibullDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (WeibullDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (WeibullDistribution)
***** error <truncate: missing input argument.> ...
 truncate (WeibullDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (WeibullDistribution, 4, 2)
***** shared pd
 pd = WeibullDistribution(1, 1);
 pd(2) = WeibullDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/dist_obj/TriangularDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/TriangularDistribution.m
***** shared pd, t
 pd = TriangularDistribution (0, 3, 5);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.0667, 0.2667, 0.6000, 0.9000, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5263, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1500, 0.2667, 0.6, 0.9, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5263, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 1.7321, 2.4495, 3, 3.5858, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.4290, 2.7928, 3.1203, 3.4945, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4495, 3, 3.5858, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.7928, 3.1203, 3.4945, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4824, 1e-4);
***** assert (iqr (t), 0.8678, 1e-4);
***** assert (mean (pd), 2.6667, 1e-4);
***** assert (mean (t), 2.9649, 1e-4);
***** assert (median (pd), 2.7386, 1e-4);
***** assert (median (t), 2.9580, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.1333, 0.2667, 0.4, 0.2, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.4211, 0.6316, 0.3158, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.0274, 1e-4);
***** assert (std (t), 0.5369, 1e-4);
***** assert (var (pd), 1.0556, 1e-4);
***** assert (var (t), 0.2882, 1e-4);
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (i, 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (Inf, 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution ([1, 2], 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution ("a", 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (NaN, 1, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, i, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, Inf, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, [1, 2], 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, "a", 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, NaN, 2)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, i)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, Inf)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, [1, 2])
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, "a")
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, NaN)
***** error <TriangularDistribution: lower limit A must be less than upper limit C.> ...
 TriangularDistribution (1, 1, 1)
***** error <TriangularDistribution: mode B must be within lower limit A and upper limit C.> ...
 TriangularDistribution (1, 0.5, 2)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (TriangularDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (TriangularDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (TriangularDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (TriangularDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (TriangularDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (TriangularDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (TriangularDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (TriangularDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (TriangularDistribution, "invalidNAME", "pdf")
***** error <'probability' PlotType is not supported for 'TriangularDistribution'.> ...
 plot (TriangularDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (TriangularDistribution)
***** error <truncate: missing input argument.> ...
 truncate (TriangularDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (TriangularDistribution, 4, 2)
***** shared pd
 pd = TriangularDistribution (0, 1, 2);
 pd(2) = TriangularDistribution (0, 1, 2);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
69 tests, 69 passed, 0 known failure, 0 skipped
[inst/dist_obj/ExponentialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/ExponentialDistribution.m
***** shared pd, t
 pd = ExponentialDistribution (1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2759, 1e-4);
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(0)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(-1)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(Inf)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(i)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution("a")
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution([1, 2])
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExponentialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExponentialDistribution, 2, 3)
***** shared x
 x = exprnd (1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (ExponentialDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", {"mu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (ExponentialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExponentialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (ExponentialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExponentialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExponentialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExponentialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (ExponentialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (ExponentialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (ExponentialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (ExponentialDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (ExponentialDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (ExponentialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (ExponentialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (ExponentialDistribution, 4, 2)
***** shared pd
 pd = ExponentialDistribution(1);
 pd(2) = ExponentialDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
90 tests, 90 passed, 0 known failure, 0 skipped
[inst/dist_obj/PiecewiseLinearDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/PiecewiseLinearDistribution.m
***** shared pd, t
 load patients
 [f, x] = ecdf (Weight);
 f = f(1:5:end);
 x = x(1:5:end);
 pd = PiecewiseLinearDistribution (x, f);
 t = truncate (pd, 130, 180);
***** assert (cdf (pd, [120, 130, 140, 150, 200]), [0.0767, 0.25, 0.4629, 0.5190, 0.9908], 1e-4);
***** assert (cdf (t, [120, 130, 140, 150, 200]), [0, 0, 0.4274, 0.5403, 1], 1e-4);
***** assert (cdf (pd, [100, 250, NaN]), [0, 1, NaN], 1e-4);
***** assert (cdf (t, [115, 290, NaN]), [0, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [111, 127.5, 136.62, 169.67, 182.17, 202], 1e-2);
***** assert (icdf (t, [0:0.2:1]), [130, 134.15, 139.26, 162.5, 173.99, 180], 1e-2);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NA, 136.62, 169.67, 182.17, 202, NA], 1e-2);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NA, 139.26, 162.5, 173.99, 180, NA], 1e-2);
***** assert (iqr (pd), 50.0833, 1e-4);
***** assert (iqr (t), 36.8077, 1e-4);
***** assert (mean (pd), 153.61, 1e-10);
***** assert (mean (t), 152.311, 1e-3);
***** assert (median (pd), 142, 1e-10);
***** assert (median (t), 141.9462, 1e-4);
***** assert (pdf (pd, [120, 130, 140, 150, 200]), [0.0133, 0.0240, 0.0186, 0.0024, 0.0046], 1e-4);
***** assert (pdf (t, [120, 130, 140, 150, 200]), [0, 0.0482, 0.0373, 0.0048, 0], 1e-4);
***** assert (pdf (pd, [100, 250, NaN]), [0, 0, NaN], 1e-4);
***** assert (pdf (t, [100, 250, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 130), false);
***** assert (any (random (t, 1000, 1) > 180), false);
***** assert (std (pd), 26.5196, 1e-4);
***** assert (std (t), 18.2941, 1e-4);
***** assert (var (pd), 703.2879, 1e-4);
***** assert (var (t), 334.6757, 1e-4);
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([0, i], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([0, Inf], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution (["a", "c"], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([NaN, 1], [0, 1])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [0, i])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [0, Inf])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], ["a", "c"])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [NaN, 1])
***** error <PiecewiseLinearDistribution: X and FX must be vectors of equal size.> ...
 PiecewiseLinearDistribution ([0, 1], [0, 0.5, 1])
***** error <PiecewiseLinearDistribution: X and FX must be at least two-elements long.> ...
 PiecewiseLinearDistribution ([0], [1])
***** error <PiecewiseLinearDistribution: FX must be bounded in the range> ...
 PiecewiseLinearDistribution ([0, 0.5, 1], [0, 1, 1.5])
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PiecewiseLinearDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PiecewiseLinearDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (PiecewiseLinearDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PiecewiseLinearDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PiecewiseLinearDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (PiecewiseLinearDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'PiecewiseLinearDistribution'.> ...
 plot (PiecewiseLinearDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (PiecewiseLinearDistribution)
***** error <truncate: missing input argument.> ...
 truncate (PiecewiseLinearDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (PiecewiseLinearDistribution, 4, 2)
***** shared pd
 pd = PiecewiseLinearDistribution ();
 pd(2) = PiecewiseLinearDistribution ();
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
63 tests, 63 passed, 0 known failure, 0 skipped
[inst/dist_obj/MultinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/MultinomialDistribution.m
***** shared pd, t
 pd = MultinomialDistribution ([0.1, 0.2, 0.3, 0.2, 0.1, 0.1]);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [2, 3, 4]), [0.3, 0.6, 0.8], eps);
***** assert (cdf (t, [2, 3, 4]), [0.2857, 0.7143, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1, 0.3, 0.6, 0.8], eps);
***** assert (cdf (pd, [1.5, 2-eps, 3, 4]), [0.1, 0.1, 0.6, 0.8], eps);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.2857, 0.7143, 1], 1e-4);
***** assert (cdf (t, [1.5, 2-eps, 3, 4]), [0, 0, 0.7143, 1], 1e-4);
***** assert (cdf (pd, [1, 2.5, 4, 6]), [0.1, 0.3, 0.8, 1], eps);
***** assert (icdf (pd, [0, 0.2857, 0.7143, 1]), [1, 2, 4, 6]);
***** assert (icdf (t, [0, 0.2857, 0.7143, 1]), [2, 2, 4, 4]);
***** assert (icdf (t, [0, 0.35, 0.7143, 1]), [2, 3, 4, 4]);
***** assert (icdf (t, [0, 0.35, 0.7143, 1, NaN]), [2, 3, 4, 4, NaN]);
***** assert (icdf (t, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 2, 3, 4, 4, NaN]);
***** assert (icdf (pd, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 1, 3, 4, 6, NaN]);
***** assert (iqr (pd), 2);
***** assert (iqr (t), 2);
***** assert (mean (pd), 3.3, 1e-14);
***** assert (mean (t), 3, eps);
***** assert (median (pd), 3);
***** assert (median (t), 3);
***** assert (pdf (pd, [-5, 1, 2.5, 4, 6, NaN, 9]), [0, 0.1, 0, 0.2, 0.1, NaN, 0]);
***** assert (pdf (pd, [-5, 1, 2, 3, 4, 6, NaN, 9]), ...
 [0, 0.1, 0.2, 0.3, 0.2, 0.1, NaN, 0]);
***** assert (pdf (t, [-5, 1, 2, 3, 4, 6, NaN, 0]), ...
 [0, 0, 0.2857, 0.4286, 0.2857, 0, NaN, 0], 1e-4);
***** assert (pdf (t, [-5, 1, 2, 4, 6, NaN, 0]), ...
 [0, 0, 0.2857, 0.2857, 0, NaN, 0], 1e-4);
***** assert (unique (random (pd, 1000, 5)), [1, 2, 3, 4, 5, 6]');
***** assert (unique (random (t, 1000, 5)), [2, 3, 4]');
***** assert (std (pd), 1.4177, 1e-4);
***** assert (std (t), 0.7559, 1e-4);
***** assert (var (pd), 2.0100, 1e-4);
***** assert (var (t), 0.5714, 1e-4);
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(0)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(-1)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(Inf)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(i)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution("a")
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution([1, 2])
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (MultinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (MultinomialDistribution, 2, 3)
***** error <cdf: X must be real.> ...
 cdf (MultinomialDistribution, i)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (MultinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (MultinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (MultinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (MultinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (MultinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (MultinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (MultinomialDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'MultinomialDistribution'.> ...
 plot (MultinomialDistribution, "PlotType", "probability")
***** error <truncate: is_nan input argument.> ...
 truncate (MultinomialDistribution)
***** error <truncate: is_nan input argument.> ...
 truncate (MultinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (MultinomialDistribution, 4, 2)
***** shared pd
 pd = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
 pd(2) = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
64 tests, 64 passed, 0 known failure, 0 skipped
[inst/dist_obj/UniformDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/UniformDistribution.m
***** shared pd, t
 pd = UniformDistribution (0, 5);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.3, 0.4, 0.6, 0.8, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 1, 2, 3, 4, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.4, 2.8, 3.2, 3.6, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 4, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.8, 3.2, 3.6, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.5, 1e-14);
***** assert (iqr (t), 1, 1e-14);
***** assert (mean (pd), 2.5, 1e-14);
***** assert (mean (t), 3, 1e-14);
***** assert (median (pd), 2.5, 1e-14);
***** assert (median (t), 3, 1e-14);
***** assert (pdf (pd, [0:5]), [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.5, 0.5, 0.5, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.4434, 1e-4);
***** assert (std (t), 0.5774, 1e-4);
***** assert (var (pd), 2.0833, 1e-4);
***** assert (var (t), 0.3333, 1e-4);
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (i, 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (Inf, 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution ([1, 2], 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution ("a", 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (NaN, 1)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, i)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, Inf)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, [1, 2])
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, "a")
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, NaN)
***** error <UniformDistribution: LOWER must be less than UPPER.> ...
 UniformDistribution (2, 1)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (UniformDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (UniformDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (UniformDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (UniformDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (UniformDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (UniformDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (UniformDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (UniformDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (UniformDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'UniformDistribution'.> ...
 plot (UniformDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (UniformDistribution)
***** error <truncate: missing input argument.> ...
 truncate (UniformDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (UniformDistribution, 4, 2)
***** shared pd
 pd = UniformDistribution (0, 1);
 pd(2) = UniformDistribution (0, 2);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
63 tests, 63 passed, 0 known failure, 0 skipped
[inst/dist_obj/GeneralizedParetoDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/GeneralizedParetoDistribution.m
***** shared pd, t
 pd = GeneralizedParetoDistribution (1, 1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0, 0.5, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6667, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.3333, 0.5, 0.6667, 0.75], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6667, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [1, 1.25, 1.6667, 2.5, 5, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2222, 2.5, 2.8571, 3.3333, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.6667, 2.5, 5, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5, 2.8571, 3.3333, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9143, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.7726, 1e-4);
***** assert (median (pd), 2);
***** assert (median (t), 2.6667, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 1, 0.25, 0.1111, 0.0625, 0.04], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1, 0.4444, 0.25, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 1, 0.25, 0.1111, 0.0625, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1, 0.4444, 0.25, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5592, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3128, 1e-4);
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution(Inf, 1, 1)
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution(i, 1, 1)
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution("a", 1, 1)
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution([1, 2], 1, 1)
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution(NaN, 1, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, 0, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, -1, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, Inf, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, i, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, "a", 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, [1, 2], 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, NaN, 1)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, Inf)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, i)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, "a")
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, [1, 2])
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedParetoDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedParetoDistribution, 2, 3)
***** shared x
 x = gprnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), ...
          "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), ...
          "parameter", {"k", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", {"k", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GeneralizedParetoDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedParetoDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedParetoDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GeneralizedParetoDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GeneralizedParetoDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GeneralizedParetoDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ...
          "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedParetoDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedParetoDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GeneralizedParetoDistribution, 4, 2)
***** shared pd
 pd = GeneralizedParetoDistribution(1, 1, 1);
 pd(2) = GeneralizedParetoDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
100 tests, 100 passed, 0 known failure, 0 skipped
[inst/dist_obj/LoguniformDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/LoguniformDistribution.m
***** shared pd, t
 pd = LoguniformDistribution (1, 4);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0, 0.5, 0.7925, 1, 1], 1e-4);
***** assert (cdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0, 0.5850, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.2925, 0.5, 0.7925, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.5850, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [1, 1.3195, 1.7411, 2.2974, 3.0314, 4], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2974, 2.6390, 3.0314, 3.4822, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.7411, 2.2974, 3.0314, 4, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.6390, 3.0314, 3.4822, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4142, 1e-4);
***** assert (iqr (t), 0.9852, 1e-4);
***** assert (mean (pd), 2.1640, 1e-4);
***** assert (mean (t), 2.8854, 1e-4);
***** assert (median (pd), 2);
***** assert (median (t), 2.8284, 1e-4);
***** assert (pdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0.7213, 0.3607, 0.2404, 0.1803, 0], 1e-4);
***** assert (pdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0.7213, 0.4809, 0.3607, 0], 1e-4);
***** assert (pdf (pd, [-1, 1, 2, 3, 4, NaN]), [0, 0.7213, 0.3607, 0.2404, 0.1803, NaN], 1e-4);
***** assert (pdf (t, [-1, 1, 2, 3, 4, NaN]), [0, 0, 0.7213, 0.4809, 0.3607, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (pd, 1000, 1) < 1), false);
***** assert (any (random (pd, 1000, 1) > 4), false);
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.8527, 1e-4);
***** assert (std (t), 0.5751, 1e-4);
***** assert (var (pd), 0.7270, 1e-4);
***** assert (var (t), 0.3307, 1e-4);
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (i, 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (Inf, 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution ([1, 2], 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution ("a", 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (NaN, 1)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, i)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, Inf)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, [1, 2])
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, "a")
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, NaN)
***** error <LoguniformDistribution: LOWER must be less than UPPER.> ...
 LoguniformDistribution (2, 1)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoguniformDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoguniformDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LoguniformDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoguniformDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LoguniformDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoguniformDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoguniformDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoguniformDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LoguniformDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'LoguniformDistribution'.> ...
 plot (LoguniformDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (LoguniformDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LoguniformDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LoguniformDistribution, 4, 2)
***** shared pd
 pd = LoguniformDistribution(1, 4);
 pd(2) = LoguniformDistribution(2, 5);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
65 tests, 65 passed, 0 known failure, 0 skipped
[inst/dist_obj/PoissonDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/PoissonDistribution.m
***** shared pd, t, t_inf
 pd = PoissonDistribution;
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.3679, 0.7358, 0.9197, 0.9810, 0.9963, 0.9994], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.7059, 0.9412, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.6961, 0.9281, 0.9861, 0.9978], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7358, 0.9197, 0.9810, 0.9963], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.7059, 0.9412, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0, 1, 1, 2, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 2, 3, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 2, 2, 3, Inf], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1, 1, 2, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 2, 3, 4, NaN], 1e-4);
***** assert (iqr (pd), 2);
***** assert (iqr (t), 1);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.3529, 1e-4);
***** assert (mean (t_inf), 2.3922, 1e-4);
***** assert (median (pd), 1);
***** assert (median (t), 2);
***** assert (median (t_inf), 2);
***** assert (pdf (pd, [0:5]), [0.3679, 0.3679, 0.1839, 0.0613, 0.0153, 0.0031], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.7059, 0.2353, 0.0588, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.6961, 0.2320, 0.0580, 0.0116], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1839, 0.0613, 0.0153, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.7059, 0.2353, 0.0588, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5882, 1e-4);
***** assert (std (t_inf), 0.6738, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.3460, 1e-4);
***** assert (var (t_inf), 0.4540, 1e-4);
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(0)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(-1)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(Inf)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(i)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution("a")
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution([1, 2])
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PoissonDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PoissonDistribution, 2, 3)
***** shared x
 x = poissrnd (1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (PoissonDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "parameter", "lambda", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (PoissonDistribution.fit (x), "parameter", {"lambda", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"lambda", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (PoissonDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "lambda", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (PoissonDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PoissonDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (PoissonDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PoissonDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PoissonDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PoissonDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (PoissonDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (PoissonDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (PoissonDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (PoissonDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (PoissonDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (PoissonDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (PoissonDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (PoissonDistribution)
***** error <truncate: missing input argument.> ...
 truncate (PoissonDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (PoissonDistribution, 4, 2)
***** shared pd
 pd = PoissonDistribution(1);
 pd(2) = PoissonDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/dist_obj/HalfNormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/HalfNormalDistribution.m
***** shared pd, t
 pd = HalfNormalDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6827, 0.9545, 0.9973, 0.9999, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9420, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8664, 0.9545, 0.9973, 0.9999], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9420, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2533, 0.5244, 0.8416, 1.2816, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0923, 2.2068, 2.3607, 2.6064, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5244, 0.8416, 1.2816, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2068, 2.3607, 2.6064, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.8317, 1e-4);
***** assert (iqr (t), 0.4111, 1e-4);
***** assert (mean (pd), 0.7979, 1e-4);
***** assert (mean (t), 2.3706, 1e-4);
***** assert (median (pd), 0.6745, 1e-4);
***** assert (median (t), 2.2771, 1e-4);
***** assert (pdf (pd, [0:5]), [0.7979, 0.4839, 0.1080, 0.0089, 0.0003, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 2.3765, 0.1951, 0.0059, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.4839, 0.1080, 0.0089, 0.0003, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 2.3765, 0.1951, 0.0059, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.6028, 1e-4);
***** assert (std (t), 0.3310, 1e-4);
***** assert (var (pd), 0.3634, 1e-4);
***** assert (var (t), 0.1096, 1e-4);
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(Inf, 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(i, 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution("a", 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution([1, 2], 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(NaN, 1)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, 0)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, -1)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, Inf)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, i)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, "a")
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, [1, 2])
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (HalfNormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (HalfNormalDistribution, 2, 3)
***** shared x
 x = hnrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "parameter", "sigma", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1),"NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (HalfNormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (HalfNormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (HalfNormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (HalfNormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (HalfNormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (HalfNormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (HalfNormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (HalfNormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (HalfNormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), {1})
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 1)
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (HalfNormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (HalfNormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (HalfNormalDistribution, 4, 2)
***** shared pd
 pd = HalfNormalDistribution(1, 1);
 pd(2) = HalfNormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/RicianDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/RicianDistribution.m
***** shared pd, t
 pd = RicianDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.2671, 0.7310, 0.9563, 0.9971, 0.9999], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.8466, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.5120, 0.7310, 0.9563, 0.9971, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.8466, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.8501, 1.2736, 1.6863, 2.2011, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1517, 2.3296, 2.5545, 2.8868, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.2736, 1.6863, 2.2011, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3296, 2.5545, 2.8868, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0890, 1e-4);
***** assert (iqr (t), 0.5928, 1e-4);
***** assert (mean (pd), 1.5486, 1e-4);
***** assert (mean (t), 2.5380, 1e-4);
***** assert (median (pd), 1.4755, 1e-4);
***** assert (median (t), 2.4341, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.4658, 0.3742, 0.0987, 0.0092, 0.0003], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.4063, 0.3707, 0.0346, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4864, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.7758, 1e-4);
***** assert (std (t), 0.4294, 1e-4);
***** assert (var (pd), 0.6019, 1e-4);
***** assert (var (t), 0.1844, 1e-4);
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(-eps, 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(-1, 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(Inf, 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(i, 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution("a", 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution([1, 2], 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(NaN, 1)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, 0)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, -1)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, Inf)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, i)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, "a")
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, [1, 2])
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RicianDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RicianDistribution, 2, 3)
***** shared x
 x = gevrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (RicianDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "parameter", "s", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RicianDistribution.fit (x), "parameter", {"s", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"s", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (RicianDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "s", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (RicianDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RicianDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (RicianDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RicianDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RicianDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RicianDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (RicianDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (RicianDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (RicianDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (RicianDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (RicianDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RicianDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RicianDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (RicianDistribution)
***** error <truncate: missing input argument.> ...
 truncate (RicianDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (RicianDistribution, 4, 2)
***** shared pd
 pd = RicianDistribution(1, 1);
 pd(2) = RicianDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/dist_obj/BurrDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/BurrDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Burr type XII
 ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
 ## XII distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data

 pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
 rand ("seed", 21);
 data = random (pd, 5000, 1);
 pd = fitdist (data, "Burr")
 plot (pd)
 msg = strcat (["Fitted Burr type XII distribution with"], ...
                [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
 title (sprintf (msg, pd.alpha, pd.c, pd.k))
***** demo
 ## Plot the PDF of a Burr type XII distribution, with parameters alpha = 1,
 ## c = 2, and k = 1, truncated at [0, 2] intervals. Generate 10000 random
 ## samples from this truncated distribution and superimpose a histogram with
 ## 100 bins scaled accordingly

 pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
 t = truncate (pd, 0.5, 2.5)
 rand ("seed", 21);
 data = random (t, 10000, 1);
 plot (t)
 title ("Burr type XII distribution (alpha = 1, c = 2, k = 1) truncated at [0.5, 2.5]")
 hold on
 hist (data, 100, 50)
 hold off
***** demo
 ## Generate a data set of 100 random samples from a Burr type XII
 ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
 ## XII  distribution to this data and plot its CDF superimposed over an
 ## empirical CDF of the data

 pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
 rand ("seed", 21);
 data = random (pd, 100, 1);
 pd = fitdist (data, "Burr")
 plot (pd, "plottype", "cdf")
 msg = strcat (["Fitted Burr type XII distribution with"], ...
                [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
 title (sprintf (msg, pd.alpha, pd.c, pd.k))
 legend ({"empirical CDF", "fitted CDF"}, "location", "east")
***** shared pd, t
 pd = BurrDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9524, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.8312, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.75, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5674, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3220, 1e-4);
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(0, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(-1, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(Inf, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(i, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution("a", 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution([1, 2], 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(NaN, 1, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, 0, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, -1, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, Inf, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, i, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, "a", 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, [1, 2], 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, NaN, 1)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, 0)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, -1)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, Inf)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, i)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, "a")
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, [1, 2])
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BurrDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BurrDistribution, 2, 3)
***** shared x
 rand ("seed", 4);
 x = burrrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BurrDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "parameter", "c", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BurrDistribution.fit (x), "parameter", {"alpha", "c", "k", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"alpha", "c", "k", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BurrDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "c", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BurrDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BurrDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BurrDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BurrDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BurrDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BurrDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BurrDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BurrDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BurrDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BurrDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BurrDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BurrDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BurrDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BurrDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BurrDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BurrDistribution, 4, 2)
***** shared pd
 pd = BurrDistribution(1, 1, 1);
 pd(2) = BurrDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
104 tests, 104 passed, 0 known failure, 0 skipped
[inst/dist_obj/LoglogisticDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/LoglogisticDistribution.m
***** shared pd, t
 pd = LoglogisticDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9524, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.8312, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.75, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5674, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3220, 1e-4);
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(Inf, 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(i, 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution("a", 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution([1, 2], 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(NaN, 1)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, 0)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, -1)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, Inf)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, i)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, "a")
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, [1, 2])
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoglogisticDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoglogisticDistribution, 2, 3)
***** shared x
 x = loglrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", {"mu", "sigma", "pa"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LoglogisticDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoglogisticDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LoglogisticDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoglogisticDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoglogisticDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoglogisticDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LoglogisticDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LoglogisticDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LoglogisticDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LoglogisticDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LoglogisticDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LoglogisticDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LoglogisticDistribution, 4, 2)
***** shared pd
 pd = LoglogisticDistribution(1, 1);
 pd(2) = LoglogisticDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/GeneralizedExtremeValueDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/GeneralizedExtremeValueDistribution.m
***** shared pd, t
 pd = GeneralizedExtremeValueDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.3679, 0.6922, 0.8734, 0.9514, 0.9819, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7195, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8, 0.8734, 0.9514, 0.9819], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7195, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.4759, 0.0874, 0.6717, 1.4999, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1999, 2.4433, 2.7568, 3.2028, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.0874, 0.6717, 1.4999, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4433, 2.7568, 3.2028, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.5725, 1e-4);
***** assert (iqr (t), 0.8164, 1e-4);
***** assert (mean (pd), 0.5772, 1e-4);
***** assert (mean (t), 2.7043, 1e-4);
***** assert (median (pd), 0.3665, 1e-4);
***** assert (median (t), 2.5887, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3679, 0.2546, 0.1182, 0.0474, 0.0180, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.0902, 0.4369, 0.1659, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1794, 0.2546, 0.1182, 0.0474, 0.0180, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0902, 0.4369, 0.1659, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2825, 1e-4);
***** assert (std (t), 0.5289, 1e-4);
***** assert (var (pd), 1.6449, 1e-4);
***** assert (var (t), 0.2798, 1e-4);
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(Inf, 1, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(i, 1, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution("a", 1, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution([1, 2], 1, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(NaN, 1, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 0, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, -1, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, Inf, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, i, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, "a", 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, [1, 2], 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, NaN, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, Inf)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, i)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, "a")
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, [1, 2])
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedExtremeValueDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedExtremeValueDistribution, 2, 3)
***** shared x
 x = gevrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), ...
          "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), ...
          "parameter", {"k", "sigma", "mu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"k", "sigma", "mu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GeneralizedExtremeValueDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GeneralizedExtremeValueDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ...
          "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedExtremeValueDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedExtremeValueDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GeneralizedExtremeValueDistribution, 4, 2)
***** shared pd
 pd = GeneralizedExtremeValueDistribution(1, 1, 1);
 pd(2) = GeneralizedExtremeValueDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
100 tests, 100 passed, 0 known failure, 0 skipped
[inst/dist_obj/LognormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/LognormalDistribution.m
***** shared pd, t
 pd = LognormalDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7559, 0.8640, 0.9172, 0.9462], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6705, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6574, 0.7559, 0.8640, 0.9172], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6705, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4310, 0.7762, 1.2883, 2.3201, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2256, 2.5015, 2.8517, 3.3199, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7762, 1.2883, 2.3201, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5015, 2.8517, 3.3199, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4536, 1e-4);
***** assert (iqr (t), 0.8989, 1e-4);
***** assert (mean (pd), 1.6487, 1e-4);
***** assert (mean (t), 2.7692, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.6653, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1569, 0.0727, 0.0382, 0.0219], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.9727, 0.4509, 0.2366, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1569, 0.0727, 0.0382, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.9727, 0.4509, 0.2366, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 2.1612, 1e-4);
***** assert (std (t), 0.5540, 1e-4);
***** assert (var (pd), 4.6708, 1e-4);
***** assert (var (t), 0.3069, 1e-4);
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(Inf, 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(i, 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution("a", 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution([1, 2], 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(NaN, 1)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, 0)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, -1)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, Inf)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, i)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, "a")
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, [1, 2])
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LognormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LognormalDistribution, 2, 3)
***** shared x
 randn ("seed", 1);
 x = lognrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LognormalDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LognormalDistribution.fit (x), "parameter", {"mu", "sigma", "parm"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LognormalDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LognormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LognormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LognormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LognormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LognormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LognormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LognormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LognormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LognormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LognormalDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LognormalDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LognormalDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LognormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LognormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LognormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LognormalDistribution, 4, 2)
***** shared pd
 pd = LognormalDistribution(1, 1);
 pd(2) = LognormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/BinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/BinomialDistribution.m
***** shared pd, t, t_inf
 pd = BinomialDistribution (5, 0.5);
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.0312, 0.1875, 0.5, 0.8125, 0.9688, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.4, 0.8, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.3846, 0.7692, 0.9615, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1875, 0.5, 0.8125, 0.9688, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0.4, 0.8, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 2, 2, 3, 3, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 3, 3, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 3, 3, 4, 5], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 4, NaN], 1e-4);
***** assert (iqr (pd), 1);
***** assert (iqr (t), 1);
***** assert (mean (pd), 2.5, 1e-10);
***** assert (mean (t), 2.8, 1e-10);
***** assert (mean (t_inf), 2.8846, 1e-4);
***** assert (median (pd), 2.5);
***** assert (median (t), 3);
***** assert (pdf (pd, [0:5]), [0.0312, 0.1562, 0.3125, 0.3125, 0.1562, 0.0312], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.4, 0.4, 0.2, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.3846, 0.3846, 0.1923, 0.0385], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.1180, 1e-4);
***** assert (std (t), 0.7483, 1e-4);
***** assert (std (t_inf), 0.8470, 1e-4);
***** assert (var (pd), 1.2500, 1e-4);
***** assert (var (t), 0.5600, 1e-4);
***** assert (var (t_inf), 0.7175, 1e-4);
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(Inf, 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(i, 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution("a", 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution([1, 2], 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(NaN, 0.5)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, 1.01)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, -0.01)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, Inf)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, i)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, "a")
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, [1, 2])
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BinomialDistribution, 2, 3)
***** shared x
 rand ("seed", 2);
 x = binornd (5, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "p", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), ...
          "parameter", {"N", "p", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", {"N", "p", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "N")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", "p", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BinomialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BinomialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BinomialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BinomialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BinomialDistribution, 4, 2)
***** shared pd
 pd = BinomialDistribution(1, 0.5);
 pd(2) = BinomialDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/GammaDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/GammaDistribution.m
***** shared pd, t
 pd = GammaDistribution (1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2759, 1e-4);
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(0, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(Inf, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(i, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution("a", 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution([1, 2], 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(NaN, 1)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, 0)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, -1)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, Inf)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, i)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, "a")
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, [1, 2])
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GammaDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GammaDistribution, 2, 3)
***** shared x
 x = gamrnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GammaDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GammaDistribution.fit (x), "parameter", {"a", "b", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"a", "b", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GammaDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GammaDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GammaDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GammaDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GammaDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GammaDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GammaDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GammaDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GammaDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GammaDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GammaDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GammaDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GammaDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GammaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GammaDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GammaDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GammaDistribution, 4, 2)
***** shared pd
 pd = GammaDistribution(1, 1);
 pd(2) = GammaDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/ExtremeValueDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/ExtremeValueDistribution.m
***** shared pd, t
 pd = ExtremeValueDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.6321, 0.9340, 0.9994, 1, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 1, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9887, 0.9994, 1, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 1, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.4999, -0.6717, -0.0874, 0.4759, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0298, 2.0668, 2.1169, 2.1971, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.6717, -0.0874, 0.4759, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.0668, 2.1169, 2.1971, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.5725, 1e-4);
***** assert (iqr (t), 0.1338, 1e-4);
***** assert (mean (pd), -0.5772, 1e-4);
***** assert (mean (t), 2.1206, 1e-4);
***** assert (median (pd), -0.3665, 1e-4);
***** assert (median (t), 2.0897, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3679, 0.1794, 0.0046, 0, 0, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 7.3891, 0.0001, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2546, 0.1794, 0.0046, 0, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 7.3891, 0.0001, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2825, 1e-4);
***** assert (std (t), 0.1091, 1e-4);
***** assert (var (pd), 1.6449, 1e-4);
***** assert (var (t), 0.0119, 1e-4);
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(Inf, 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(i, 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution("a", 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution([1, 2], 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(NaN, 1)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, 0)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, -1)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, Inf)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, i)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, "a")
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, [1, 2])
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExtremeValueDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExtremeValueDistribution, 2, 3)
***** shared x
 rand ("seed", 1);
 x = evrnd (1, 1, [1000, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), ...
          "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExtremeValueDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (ExtremeValueDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExtremeValueDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExtremeValueDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (ExtremeValueDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (ExtremeValueDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (ExtremeValueDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (ExtremeValueDistribution)
***** error <truncate: missing input argument.> ...
 truncate (ExtremeValueDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (ExtremeValueDistribution, 4, 2)
***** shared pd
 pd = ExtremeValueDistribution(1, 1);
 pd(2) = ExtremeValueDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/NegativeBinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/NegativeBinomialDistribution.m
***** shared pd, t, t_inf
 pd = NegativeBinomialDistribution (5, 0.5);
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.0312, 0.1094, 0.2266, 0.3633, 0.5, 0.6230], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.3, 0.65, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.1316, 0.2851, 0.4386, 0.5768], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1094, 0.2266, 0.3633, 0.5000], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.3, 0.65, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 2, 4, 5, 7, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 3, 3, 4, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 3, 4, 6, 8, Inf], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 4, 5, 7, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 3, 3, 4, 4, NaN], 1e-4);
***** assert (iqr (pd), 4);
***** assert (iqr (t), 2);
***** assert (mean (pd), 5);
***** assert (mean (t), 3.0500, 1e-4);
***** assert (mean (t_inf), 5.5263, 1e-4);
***** assert (median (pd), 4);
***** assert (median (t), 3);
***** assert (pdf (pd, [0:5]), [0.0312, 0.0781, 0.1172, 0.1367, 0.1367, 0.1230], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.3, 0.35, 0.35, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.1316, 0.1535, 0.1535, 0.1382], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.0781, 0.1172, 0.1367, 0.1367, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.3, 0.35, 0.35, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 3.1623, 1e-4);
***** assert (std (t), 0.8047, 1e-4);
***** assert (std (t_inf), 2.9445, 1e-4);
***** assert (var (pd), 10);
***** assert (var (t), 0.6475, 1e-4);
***** assert (var (t_inf), 8.6704, 1e-4);
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(Inf, 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(i, 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution("a", 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution([1, 2], 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(NaN, 1)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, 0)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, -1)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, Inf)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, i)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, "a")
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, [1, 2])
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, NaN)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, 1.2)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NegativeBinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NegativeBinomialDistribution, 2, 3)
***** shared x
 x = nbinrnd (1, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "parameter", "R", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), ...
          "parameter", {"R", "P", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"R", "P", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NegativeBinomialDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "R", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NegativeBinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NegativeBinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NegativeBinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NegativeBinomialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NegativeBinomialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NegativeBinomialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NegativeBinomialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NegativeBinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NegativeBinomialDistribution, 4, 2)
***** shared pd
 pd = NegativeBinomialDistribution(1, 0.5);
 pd(2) = NegativeBinomialDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/InverseGaussianDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/InverseGaussianDistribution.m
***** shared pd, t
 pd = InverseGaussianDistribution (1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6681, 0.8855, 0.9532, 0.9791, 0.9901], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7234, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8108, 0.8855, 0.9532, 0.9791], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7234, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.3320, 0.5411, 0.8483, 1.4479, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1889, 2.4264, 2.7417, 3.1993, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5411, 0.8483, 1.4479, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4264, 2.7417, 3.1993, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.8643, 1e-4);
***** assert (iqr (t), 0.8222, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6953, 1e-4);
***** assert (median (pd), 0.6758, 1e-4);
***** assert (median (t), 2.5716, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1098, 0.0394, 0.0162, 0.0072], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1736, 0.4211, 0.1730, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1098, 0.0394, 0.0162, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1736, 0.4211, 0.1730, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5332, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2843, 1e-4);
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(0, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(Inf, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(i, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution("a", 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution([1, 2], 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(NaN, 1)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, 0)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, -1)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, Inf)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, i)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, "a")
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, [1, 2])
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (InverseGaussianDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (InverseGaussianDistribution, 2, 3)
***** shared x
 x = invgrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), ...
          "parameter", {"mu", "lambda", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "lambda", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (InverseGaussianDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (InverseGaussianDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (InverseGaussianDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (InverseGaussianDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (InverseGaussianDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (InverseGaussianDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (InverseGaussianDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (InverseGaussianDistribution)
***** error <truncate: missing input argument.> ...
 truncate (InverseGaussianDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (InverseGaussianDistribution, 4, 2)
***** shared pd
 pd = InverseGaussianDistribution(1, 1);
 pd(2) = InverseGaussianDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/confusionmat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/confusionmat.m
***** test
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 C  = [0 1 1 0 0 0 0 0; 0 0 0 0 1 0 0 0; 0 1 0 0 0 0 1 0; 0 0 0 1 0 1 0 0; ...
       0 0 0 0 3 0 0 0; 0 0 0 1 0 1 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 2];
 assert (confusionmat (Yt, Yp), C)
1 test, 1 passed, 0 known failure, 0 skipped
[inst/logistic_regression.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/logistic_regression.m
***** test
 # Output compared to following MATLAB commands
 # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
 # P = mnrval(B,X)
 X = [1.489381332449196, 1.1534152241851305; ...
      1.8110085304863965, 0.9449666896938425; ...
      -0.04453299665130296, 0.34278203449678646; ...
      -0.36616019468850347, 1.130254275908322; ...
       0.15339143291005095, -0.7921044310668951; ...
      -1.6031878794469698, -1.8343471035233376; ...
      -0.14349521143198166, -0.6762996896828459; ...
      -0.4403818557740143, -0.7921044310668951; ...
      -0.7372685001160434, -0.027793137932169563; ...
      -0.11875465773681024, 0.5512305689880763];
 Y = [1,1,1,1,1,0,0,0,0,0]';
 [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (Y, X, false);
***** test
 # Output compared to following MATLAB commands
 # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
 load carbig
 X = [Acceleration Displacement Horsepower Weight];
 miles = [1,1,1,1,1,1,1,1,1,1,NaN,NaN,NaN,NaN,NaN,1,1,NaN,1,1,2,2,1,2,2,2, ...
          2,2,2,2,2,1,1,1,1,2,2,2,2,NaN,2,1,1,2,1,1,1,1,1,1,1,1,1,2,2,1,2, ...
          2,3,3,3,3,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,2,2,2,2, ...
          2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,1,1,1,1,1,2,2,2,1,2,2, ...
          2,1,1,3,2,2,2,1,2,2,1,2,2,2,1,3,2,3,2,1,1,1,1,1,1,1,1,3,2,2,3,3, ...
          2,2,2,2,2,3,2,1,1,1,1,1,1,1,1,1,1,1,2,2,1,3,2,2,2,2,2,2,1,3,2,2, ...
          2,2,2,3,2,2,2,2,2,1,1,1,1,2,2,2,2,3,2,3,3,2,1,1,1,3,3,2,2,2,1,2, ...
          2,1,1,1,1,1,3,3,3,2,3,1,1,1,1,1,2,2,1,1,1,1,1,3,2,2,2,3,3,3,3,2, ...
          2,2,4,3,3,4,3,2,2,2,2,2,2,2,2,2,2,2,1,1,2,1,1,1,3,2,2,3,2,2,2,2, ...
          2,1,2,1,3,3,2,2,2,2,2,1,1,1,1,1,1,2,1,3,3,3,2,2,2,2,2,3,3,3,3,2, ...
          2,2,3,4,3,3,3,2,2,2,2,3,3,3,3,3,4,2,4,4,4,3,3,4,4,3,3,3,2,3,2,3, ...
          2,2,2,2,3,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,NaN,3,2,2,2,2,2,1,2, ...
          2,3,3,3,2,2,2,3,3,3,3,3,3,3,3,3,3,3,2,3,2,2,3,3,2,2,4,3,2,3]';
 [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (miles, X, false);
 assert (DEV, 433.197174495549, 1e-05);
 assert (INTERCEPT(1), -16.6895155618903, 1e-05);
 assert (INTERCEPT(2), -11.7207818178493, 1e-05);
 assert (INTERCEPT(3), -8.0605768506075, 1e-05);
 assert (SLOPE(1), 0.104762463756714, 1e-05);
 assert (SLOPE(2), 0.0103357623191891, 1e-05);
 assert (SLOPE(3), 0.0645199313242276, 1e-05);
 assert (SLOPE(4), 0.00166377028388103, 1e-05);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/silhouette.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/silhouette.m
***** demo
 load fisheriris;
 X = meas(:,3:4);
 cidcs = kmeans (X, 3, "Replicates", 5);
 silhouette (X, cidcs);
 y_labels(cidcs([1 51 101])) = unique (species);
 set (gca, "yticklabel", y_labels);
 title ("Fisher's iris data");
***** error silhouette ();
***** error silhouette ([1 2; 1 1]);
***** error <X .* doesn't match .* clust> silhouette ([1 2; 1 1], [1 2 3]');
***** error <invalid metric> silhouette ([1 2; 1 1], [1 2]', "xxx");
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/evalclusters.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/evalclusters.m
***** demo
 load fisheriris;
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6])
 plot (eva)
***** error evalclusters ()
***** error evalclusters ([1 1;0 1])
***** error evalclusters ([1 1;0 1], "kmeans")
***** error <'x' must be a numeric*> evalclusters ("abc", "kmeans", "gap")
***** error <unknown clustering*> evalclusters ([1 1;0 1], "xxx", "gap")
***** error <invalid matrix*> evalclusters ([1 1;0 1], [1 2], "gap")
***** error <invalid argument*> evalclusters ([1 1;0 1], 1.2, "gap")
***** error <invalid criterion*> evalclusters ([1 1;0 1], [1; 2], 123)
***** error <unknown criterion*> evalclusters ([1 1;0 1], [1; 2], "xxx")
***** error <'KList' can be empty*> evalclusters ([1 1;0 1], "kmeans", "gap")
***** error <invalid parameter*> evalclusters ([1 1;0 1], [1; 2], "gap", 1)
***** error <invalid property*> evalclusters ([1 1;0 1], [1; 2], "gap", 1, 1)
***** error <unknown property*> evalclusters ([1 1;0 1], [1; 2], "gap", "xxx", 1)
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [-1 0])
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 .5])
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 1; 1 1])
***** error <unknown distance*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
                                        "distance", "a")
***** error <distance metric*> evalclusters ([1 1;0 1], [1; 2], "daviesbouldin", ...
                                       "distance", "a")
***** error <cluster prior*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
                                     "clusterpriors", "equal")
***** error <invalid cluster prior*> evalclusters ([1 1;0 1], [1; 2], ...
                                         "silhouette", "clusterpriors", "xxx")
***** error <'clust' must be a clustering*> evalclusters ([1 1;0 1], [1; 2], "gap")
***** test
 load fisheriris;
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
 assert (isa (eva, "CalinskiHarabaszEvaluation"));
 assert (eva.NumObservations, 150);
 assert (eva.OptimalK, 3);
 assert (eva.InspectedK, [1 2 3 4 5 6]);
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/rangesearch.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/rangesearch.m
***** demo
 ## Generate 1000 random 2D points from each of five distinct multivariate
 ## normal distributions that form five separate classes
 N = 1000;
 d = 10;
 randn ("seed", 5);
 X1 = mvnrnd (d * [0, 0], eye (2), 1000);
 randn ("seed", 6);
 X2 = mvnrnd (d * [1, 1], eye (2), 1000);
 randn ("seed", 7);
 X3 = mvnrnd (d * [-1, -1], eye (2), 1000);
 randn ("seed", 8);
 X4 = mvnrnd (d * [1, -1], eye (2), 1000);
 randn ("seed", 8);
 X5 = mvnrnd (d * [-1, 1], eye (2), 1000);
 X = [X1; X2; X3; X4; X5];

 ## For each point in X, find the points in X that are within a radius d
 ## away from the points in X.
 Idx = rangesearch (X, X, d, "NSMethod", "exhaustive");

 ## Select the first point in X (corresponding to the first class) and find
 ## its nearest neighbors within the radius d.  Display these points in
 ## one color and the remaining points in a different color.
 x = X(1,:);
 nearestPoints = X (Idx{1},:);
 nonNearestIdx = true (size (X, 1), 1);
 nonNearestIdx(Idx{1}) = false;

 scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
 hold on
 scatter (nearestPoints(:,1),nearestPoints(:,2))
 scatter (x(1), x(2), "black", "filled")
 hold off

 ## Select the last point in X (corresponding to the fifth class) and find
 ## its nearest neighbors within the radius d.  Display these points in
 ## one color and the remaining points in a different color.
 x = X(end,:);
 nearestPoints = X (Idx{1},:);
 nonNearestIdx = true (size (X, 1), 1);
 nonNearestIdx(Idx{1}) = false;

 figure
 scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
 hold on
 scatter (nearestPoints(:,1),nearestPoints(:,2))
 scatter (x(1), x(2), "black", "filled")
 hold off
***** shared x, y, X, Y
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [2, 3, 4; 1, 4, 3];
 X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 Y = [1, 2, 2, 3; 2, 3, 3, 4];
***** test
 [idx, D] = rangesearch (x, y, 4);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "SortIndices", true);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive", ...
                         "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (x, y, 4, "Distance", eucldist);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (x, y, 4, "Distance", eucldist, ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[0.6024, 1.0079, 1.2047]; [0.6963, 1.2047]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive", "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[0.6024, 1.2047, 1.0079]; [0.6963, 1.2047]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 2);
 assert (idx, {[1]; [1, 2]});
 assert (D, {[1.4142]; [1.4142, 1.4142]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (X, Y, 4, "Distance", eucldist);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4, "SortIndices", false);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** error<rangesearch: too few input arguments.> rangesearch (1)
***** error<rangesearch: number of columns in X and Y must match.> ...
 rangesearch (ones (4, 5), ones (4))
***** error<rangesearch: invalid NAME in optional pairs of arguments.> ...
 rangesearch (ones (4, 2), ones (3, 2), 1, "Distance", "euclidean", "some", "some")
***** error<rangesearch: only a single distance parameter can be defined.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones (1, 5), "P", 3)
***** error<rangesearch: invalid value of Minkowski Exponent.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "P",-2)
***** error<rangesearch: invalid value in Scale or the size of Scale.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones(4,5), "distance", "euclidean")
***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ["some" "some"])
***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ones(4,5), "distance", "euclidean")
***** error<rangesearch: invalid value of bucketsize.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "bucketsize", -1)
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "cosine")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "mahalanobis")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "correlation")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "seuclidean")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "spearman")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "hamming")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "jaccard")
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/ecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ecdf.m
***** demo
 y = exprnd (10, 50, 1);    ## random failure times are exponential(10)
 d = exprnd (20, 50, 1);    ## drop-out times are exponential(20)
 t = min (y, d);            ## we observe the minimum of these times
 censored = (y > d);        ## we also observe whether the subject failed

 ## Calculate and plot the empirical cdf and confidence bounds
 [f, x, flo, fup] = ecdf (t, "censoring", censored);
 stairs (x, f);
 hold on;
 stairs (x, flo, "r:"); stairs (x, fup, "r:");

 ## Superimpose a plot of the known true cdf
 xx = 0:.1:max (t); yy = 1 - exp (-xx / 10); plot (xx, yy, "g-");
 hold off;
***** demo
 R = wblrnd (100, 2, 100, 1);
 ecdf (R, "Function", "survivor", "Alpha", 0.01, "Bounds", "on");
 hold on
 x = 1:1:250;
 wblsurv = 1 - cdf ("weibull", x, 100, 2);
 plot (x, wblsurv, "g-", "LineWidth", 2)
 legend ("Empirical survivor function", "Lower confidence bound", ...
         "Upper confidence bound", "Weibull survivor function", ...
         "Location", "northeast");
 hold off
***** error ecdf ();
***** error ecdf (randi (15,2));
***** error ecdf ([3,2,4,3+2i,5]);
***** error kstest ([2,3,4,5,6],"tail");
***** error kstest ([2,3,4,5,6],"tail", "whatever");
***** error kstest ([2,3,4,5,6],"function", "");
***** error kstest ([2,3,4,5,6],"badoption", 0.51);
***** error kstest ([2,3,4,5,6],"tail", 0);
***** error kstest ([2,3,4,5,6],"alpha", 0);
***** error kstest ([2,3,4,5,6],"alpha", NaN);
***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal");
***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1]);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
   [F, x, Flo, Fup] = ecdf (x);
   F_out = [0; 0.0714; 0.1429; 0.3571; 0.5; 0.6429; 0.7143; 0.7857; 0.9286; 1];
   assert (F, F_out, ones (10,1) * 1e-4);
   x_out = [0 0 2 3 4 5 6 7 8 9]';
   assert (x, x_out);
   Flo_out = [NaN, 0, 0, 0.1061, 0.2381, 0.3919, 0.4776, 0.5708, 0.7937, NaN]';
   assert (Flo, Flo_out, ones (10,1) * 1e-4);
   Fup_out = [NaN, 0.2063, 0.3262, 0.6081, 0.7619, 0.8939, 0.9509, 1, 1, NaN]';
   assert (Fup, Fup_out, ones (10,1) * 1e-4);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
   ecdf (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/cdfplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cdfplot.m
***** demo
 x = randn(100,1);
 cdfplot (x);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
   [hCDF, stats] = cdfplot (x);
   assert (stats.min, 2);
   assert (stats.max, 6);
   assert (stats.median, 3.5);
   assert (stats.std, 1.35400640077266, 1e-14);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = randn(100,1);
   cdfplot (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error cdfplot ();
***** error cdfplot ([x',x']);
***** error cdfplot ([NaN, NaN, NaN, NaN]);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/multcompare.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/multcompare.m
***** demo

 ## Demonstration using balanced one-way ANOVA from anova1

 x = ones (50, 4) .* [-2, 0, 1, 5];
 randn ("seed", 1);    # for reproducibility
 x = x + normrnd (0, 2, 50, 4);
 groups = {"A", "B", "C", "D"};
 [p, tbl, stats] = anova1 (x, groups, "off");
 multcompare (stats);
***** demo

 ## Demonstration using unbalanced one-way ANOVA example from anovan

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [P,ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
                                  "ControlGroup", 1, "display", "on")

***** demo

 ## Demonstration using factorial ANCOVA example from anovan

 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
 97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
 81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
 84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
 100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
 91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
 exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
             "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
 58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
 75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, "model", ...
                            [1 0 0; 0 1 0; 0 0 1; 1 1 0], "continuous", 3, ...
                            "sstype", "h", "display", "off", "contrasts", ...
                            {"simple","poly",""});

 [C, M, H, GNAMES] = multcompare (STATS, "dim", [1 2], "ctype", "holm", ...
                                  "display", "on")

***** demo

 ## Demonstration using one-way ANOVA from anovan, with fit by weighted least
 ## squares to account for heteroskedasticity.

 g = [1, 1, 1, 1, 1, 1, 1, 1, ...
      2, 2, 2, 2, 2, 2, 2, 2, ...
      3, 3, 3, 3, 3, 3, 3, 3]';

 y = [13, 16, 16,  7, 11,  5,  1,  9, ...
      10, 25, 66, 43, 47, 56,  6, 39, ...
      11, 39, 26, 35, 25, 14, 24, 17]';

 [P,ATAB,STATS] = anovan(y, g, "display", "off");
 fitted = STATS.X * STATS.coeffs(:,1); # fitted values
 b = polyfit (fitted, abs (STATS.resid), 1);
 v = polyval (b, fitted);  # Variance as a function of the fitted values
 [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1, "display", "off");
 [C, M] =  multcompare (STATS, "display", "on", "ctype", "mvt")
***** demo

 ## Demonstration of p-value adjustments to control the false discovery rate
 ## Data from Westfall (1997) JASA. 92(437):299-306

 p = [.005708; .023544; .024193; .044895; ...
       .048805; .221227; .395867; .693051; .775755];

 padj = multcompare(p,'ctype','fdr')
***** test

 ## Tests using unbalanced one-way ANOVA example from anovan and anova1

 ## Test for anovan - compare pairwise comparisons with matlab for CTYPE "lsd"

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [P, ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "lsd", ...
                                  "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);

 ## Compare "fdr" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "fdr", ...
                                  "display", "off");
 assert (C(1,6), 4.08303457454140e-05, 1e-09);
 assert (C(2,6), 1.04587348240817e-06, 1e-09);
 assert (C(3,6), 1.06397381604573e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 1.05737243156806e-01, 1e-09);
 assert (C(7,6), 2.36859139493832e-07, 1e-09);
 assert (C(8,6), 2.09859420867852e-01, 1e-09);
 assert (C(9,6), 1.36324670121399e-07, 1e-09);
 assert (C(10,6), 7.40712246958735e-06, 1e-09);

 ## Compare "hochberg" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "hochberg", ...
                                  "display", "off");
 assert (C(1,6), 1.14324968087159e-04, 1e-09);
 assert (C(2,6), 3.13762044722451e-06, 1e-09);
 assert (C(3,6), 1.91515286888231e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 2.53769383576334e-01, 1e-09);
 assert (C(7,6), 6.63205590582730e-07, 1e-09);
 assert (C(8,6), 3.77746957562134e-01, 1e-09);
 assert (C(9,6), 3.27179208291358e-07, 1e-09);
 assert (C(10,6), 2.22213674087620e-05, 1e-09);

 ## Compare "holm" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
                                  "display", "off");
 assert (C(1,6), 1.14324968087159e-04, 1e-09);
 assert (C(2,6), 3.13762044722451e-06, 1e-09);
 assert (C(3,6), 1.91515286888231e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 2.53769383576334e-01, 1e-09);
 assert (C(7,6), 6.63205590582730e-07, 1e-09);
 assert (C(8,6), 3.77746957562134e-01, 1e-09);
 assert (C(9,6), 3.27179208291358e-07, 1e-09);
 assert (C(10,6), 2.22213674087620e-05, 1e-09);

 ## Compare "scheffe" adjusted p-values to those obtained using 'scheffe' in Matlab

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "scheffe", ...
                                  "display", "off");
 assert (C(1,6), 0.00108105386141085, 1e-09);
 assert (C(2,6), 2.7779386789517e-05, 1e-09);
 assert (C(3,6), 1.3599854038198e-06, 1e-09);
 assert (C(4,6), 7.58830197867751e-13, 1e-09);
 assert (C(5,6), 0.984039948220281, 1e-09);
 assert (C(6,6), 0.539077018557706, 1e-09);
 assert (C(7,6), 5.59475764460574e-06, 1e-09);
 assert (C(8,6), 0.771173490574105, 1e-09);
 assert (C(9,6), 2.52838425729905e-06, 1e-09);
 assert (C(10,6), 0.000200719143889168, 1e-09);

 ## Compare "bonferroni" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "bonferroni", ...
                                  "display", "off");
 assert (C(1,6), 2.85812420217898e-04, 1e-09);
 assert (C(2,6), 5.22936741204085e-06, 1e-09);
 assert (C(3,6), 2.12794763209146e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 1.00000000000000e+00, 1e-09);
 assert (C(6,6), 8.45897945254446e-01, 1e-09);
 assert (C(7,6), 9.47436557975328e-07, 1e-09);
 assert (C(8,6), 1.00000000000000e+00, 1e-09);
 assert (C(9,6), 4.08974010364197e-07, 1e-09);
 assert (C(10,6), 4.44427348175241e-05, 1e-09);

 ## Test for anova1 ("equal")- comparison of results from Matlab

 [P, ATAB, STATS] = anova1 (dv, g, "off", "equal");
 [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);

 ## Test for anova1 ("unequal") - comparison with results from GraphPad Prism 8
 [P, ATAB, STATS] = anova1 (dv, g, "off", "unequal");
 [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.001247025266382, 1e-09);
 assert (C(2,6), 0.000018037115146, 1e-09);
 assert (C(3,6), 0.000002974595187, 1e-09);
 assert (C(4,6), 0.000000000786046, 1e-09);
 assert (C(5,6), 0.5693192886650109, 1e-09);
 assert (C(6,6), 0.110501699029776, 1e-09);
 assert (C(7,6), 0.000131226488700, 1e-09);
 assert (C(8,6), 0.1912101409715992, 1e-09);
 assert (C(9,6), 0.000005385256394, 1e-09);
 assert (C(10,6), 0.000074089106171, 1e-09);
***** test

 ## Test for anova2 ("interaction") - comparison with results from Matlab for column effect
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [P, ATAB, STATS] = anova2 (popcorn, 3, "off");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 1.49311100811177e-05, 1e-09);
 assert (C(2,6), 2.20506904243535e-07, 1e-09);
 assert (C(3,6), 0.00449897860490058, 1e-09);
 assert (M(1,1), 6.25, 1e-09);
 assert (M(2,1), 4.75, 1e-09);
 assert (M(3,1), 4, 1e-09);
 assert (M(1,2), 0.152145154862547, 1e-09);
 assert (M(2,2), 0.152145154862547, 1e-09);
 assert (M(3,2), 0.152145154862547, 1e-09);
***** test

 ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 [P, ATAB, STATS] = anova2 (words, 1, "off", "linear");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.000020799832702, 1e-09);
 assert (C(2,6), 0.000000035812410, 1e-09);
 assert (C(3,6), 0.003038942449215, 1e-09);
***** test

 ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
 [P, ATAB, STATS] = anova2 (data, 4, "off", "nested");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.261031111511073, 1e-09);
 assert (C(2,6), 0.065879755907745, 1e-09);
 assert (C(3,6), 0.241874613529270, 1e-09);
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 set (0, "DefaultFigureVisible", "off");

 ## Test for kruskalwallis - comparison with results from MATLAB
 data = [3,2,4; 5,4,4; 4,2,4; 4,2,4; 4,1,5; ...
         4,2,3; 4,3,5; 4,2,4; 5,2,4; 5,3,3];
 group = [1:3] .* ones (10,3);
 [P, ATAB, STATS] = kruskalwallis (data(:), group(:), "off");
 C = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.000163089828959986, 1e-09);
 assert (C(2,6), 0.630298044801257, 1e-09);
 assert (C(3,6), 0.00100567660695682, 1e-09);
 C = multcompare (STATS, "ctype", "bonferroni", "display", "off");
 assert (C(1,6), 0.000489269486879958, 1e-09);
 assert (C(2,6), 1, 1e-09);
 assert (C(3,6), 0.00301702982087047, 1e-09);
 C = multcompare(STATS, "ctype", "scheffe", "display", "off");
 assert (C(1,6), 0.000819054880289573, 1e-09);
 assert (C(2,6), 0.890628039849261, 1e-09);
 assert (C(3,6), 0.00447816059021654, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 ## Test for friedman - comparison with results from MATLAB
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [P, ATAB, STATS] = friedman (popcorn, 3, "off");
 C = multcompare(STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.227424558028569, 1e-09);
 assert (C(2,6), 0.0327204848315735, 1e-09);
 assert (C(3,6), 0.353160353315988, 1e-09);
 C = multcompare(STATS, "ctype", "bonferroni", "display", "off");
 assert (C(1,6), 0.682273674085708, 1e-09);
 assert (C(2,6), 0.0981614544947206, 1e-09);
 assert (C(3,6), 1, 1e-09);
 C = multcompare(STATS, "ctype", "scheffe", "display", "off");
 assert (C(1,6), 0.482657360384373, 1e-09);
 assert (C(2,6), 0.102266573027672, 1e-09);
 assert (C(3,6), 0.649836502233148, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 ## Test for fitlm - same comparisons as for first anovan example
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off",...
                      "contrasts","simple");
 [C, M] = multcompare(STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 ## Test p-value adjustments compared to R stats package function p.adjust
 ## Data from Westfall (1997) JASA. 92(437):299-306
 p = [.005708; .023544; .024193; .044895; ...
       .048805; .221227; .395867; .693051; .775755];
 padj = multcompare (p);
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.188352, 1e-06);
 assert (padj(3), 0.188352, 1e-06);
 assert (padj(4), 0.269370, 1e-06);
 assert (padj(5), 0.269370, 1e-06);
 assert (padj(6), 0.884908, 1e-06);
 assert (padj(7), 1.000000, 1e-06);
 assert (padj(8), 1.000000, 1e-06);
 assert (padj(9), 1.000000, 1e-06);
 padj = multcompare(p,'ctype','holm');
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.188352, 1e-06);
 assert (padj(3), 0.188352, 1e-06);
 assert (padj(4), 0.269370, 1e-06);
 assert (padj(5), 0.269370, 1e-06);
 assert (padj(6), 0.884908, 1e-06);
 assert (padj(7), 1.000000, 1e-06);
 assert (padj(8), 1.000000, 1e-06);
 assert (padj(9), 1.000000, 1e-06);
 padj = multcompare(p,'ctype','hochberg');
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.169351, 1e-06);
 assert (padj(3), 0.169351, 1e-06);
 assert (padj(4), 0.244025, 1e-06);
 assert (padj(5), 0.244025, 1e-06);
 assert (padj(6), 0.775755, 1e-06);
 assert (padj(7), 0.775755, 1e-06);
 assert (padj(8), 0.775755, 1e-06);
 assert (padj(9), 0.775755, 1e-06);
 padj = multcompare(p,'ctype','fdr');
 assert (padj(1), 0.0513720, 1e-07);
 assert (padj(2), 0.0725790, 1e-07);
 assert (padj(3), 0.0725790, 1e-07);
 assert (padj(4), 0.0878490, 1e-07);
 assert (padj(5), 0.0878490, 1e-07);
 assert (padj(6), 0.3318405, 1e-07);
 assert (padj(7), 0.5089719, 1e-07);
 assert (padj(8), 0.7757550, 1e-07);
 assert (padj(9), 0.7757550, 1e-07);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/nanmax.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/nanmax.m
***** demo
 ## Find the column maximum values and their indices
 ## for matrix data with missing values.

 x = magic (3);
 x([1, 6:9]) = NaN
 [y, ind] = nanmax (x)
***** demo
 ## Find the maximum of all the values in an array, ignoring missing values.
 ## Create a 2-by-5-by-3 array x with some missing values.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN

 ## Find the maximum of the elements of x.

 y = nanmax (x, [], 'all')
***** assert (nanmax ([2, 4, NaN, 7]), 7)
***** assert (nanmax ([2, 4, NaN, Inf]), Inf)
***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [7, 8, 6])
***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [3, 6, 8])
***** assert (nanmax (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([7, 8, 6]))
***** shared x, y
 x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
 x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
 y = x;
 y(2,3,1) = 0.51;
***** assert (nanmax (x, [], [1, 2])(:), [1.77;6.77])
***** assert (nanmax (x, [], [1, 3])(:), [6.77;5.34;NaN;5.19])
***** assert (nanmax (x, [], [2, 3])(:), [6.77;5.34])
***** assert (nanmax (x, [], [1, 2, 3]), 6.77)
***** assert (nanmax (x, [], 'all'), 6.77)
***** assert (nanmax (y, [], [1, 3])(:), [6.77;5.34;0.51;5.19])
***** assert (nanmax (x(1,:,1), x(2,:,1)), [1.77, 0.34, NaN, 0.19])
***** assert (nanmax (x(1,:,2), x(2,:,2)), [6.77, 5.34, NaN, 5.19])
***** assert (nanmax (y(1,:,1), y(2,:,1)), [1.77, 0.34, 0.51, 0.19])
***** assert (nanmax (y(1,:,2), y(2,:,2)), [6.77, 5.34, NaN, 5.19])
***** test
 xx = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (nanmax (xx, [], [3, 2])), [10, 1, 1, 3]);
 assert (size (nanmax (xx, [], [1, 2])), [1, 1, 6, 3]);
 assert (size (nanmax (xx, [], [1, 2, 4])), [1, 1, 6]);
 assert (size (nanmax (xx, [], [1, 4, 3])), [1, 40]);
 assert (size (nanmax (xx, [], [1, 2, 3, 4])), [1, 1]);
***** assert (nanmax (ones (2), [], 3), ones (2, 2))
***** assert (nanmax (ones (2, 2, 2), [], 99), ones (2, 2, 2))
***** assert (nanmax (magic (3), [], 3), magic (3))
***** assert (nanmax (magic (3), [], [1, 3]), [8, 9, 7])
***** assert (nanmax (magic (3), [], [1, 99]), [8, 9, 7])
***** assert (nanmax (ones (2), 3), 3 * ones (2,2))
***** error <nanmax: VECDIM must contain non-repeating positive integers.> ...
 nanmax (y, [], [1, 1, 2])
***** error <nanmax: a second output is not supported with this syntax.> ...
 [v, idx] = nanmax(x, y, [1 2])
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/kstest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/kstest2.m
***** error kstest2 ([1,2,3,4,5,5])
***** error kstest2 (ones(2,4), [1,2,3,4,5,5])
***** error kstest2 ([2,3,5,7,3+3i], [1,2,3,4,5,5])
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail")
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", "whatever")
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"badoption", 0.51)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", 0)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", 0)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", NaN)
***** error kstest2 ([NaN,NaN,NaN,NaN,NaN],[3;5;7;8;7;6;5],"tail", "unequal")
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2));
 assert (h, false);
 assert (p, 0.1222791870137312, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "larger");
 assert (h, false);
 assert (p, 0.1844421391011258, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller");
 assert (h, false);
 assert (p, 0.06115357930171663, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller", "alpha", 0.1);
 assert (h, true);
 assert (p, 0.06115357930171663, 1e-14);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_stat/plstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/plstat.m
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (plstat (x, Fx), 4.15)
***** test
 [m, v] = plstat (x, Fx);
 assert (v, 10.3775, 1e-14)
***** error<plstat: function called with too few input arguments.> plstat ()
***** error<plstat: function called with too few input arguments.> plstat (1)
***** error<plstat: X and FX must be vectors of equal size.> ...
 plstat ([0, 1, 2], [0, 1])
***** error<plstat: X and FX must be at least two-elements long.> ...
 plstat ([0], [1])
***** error<plstat: FX must be bounded in the range> ...
 plstat ([0, 1, 2], [0, 1, 1.5])
***** error<plstat: FX must be bounded in the range> ...
 plstat ([0, 1, 2], [0, i, 1])
***** error<plstat: X and FX must not be complex.> ...
 plstat ([0, i, 2], [0, 0.5, 1])
***** error<plstat: X and FX must not be complex.> ...
 plstat ([0, i, 2], [0, 0.5i, 1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/binostat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/binostat.m
***** error<binostat: function called with too few input arguments.> binostat ()
***** error<binostat: function called with too few input arguments.> binostat (1)
***** error<binostat: N and PS must be numeric.> binostat ({}, 2)
***** error<binostat: N and PS must be numeric.> binostat (1, "")
***** error<binostat: N and PS must not be complex.> binostat (i, 2)
***** error<binostat: N and PS must not be complex.> binostat (1, i)
***** error<binostat: N and PS must be of common size or scalars.> ...
 binostat (ones (3), ones (2))
***** error<binostat: N and PS must be of common size or scalars.> ...
 binostat (ones (2), ones (3))
***** test
 n = 1:6;
 ps = 0:0.2:1;
 [m, v] = binostat (n, ps);
 expected_m = [0.00, 0.40, 1.20, 2.40, 4.00, 6.00];
 expected_v = [0.00, 0.32, 0.72, 0.96, 0.80, 0.00];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 n = 1:6;
 [m, v] = binostat (n, 0.5);
 expected_m = [0.50, 1.00, 1.50, 2.00, 2.50, 3.00];
 expected_v = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 n = [-Inf -3 5 0.5 3 NaN 100, Inf];
 [m, v] = binostat (n, 0.5);
 assert (isnan (m), [true true false true false true false false])
 assert (isnan (v), [true true false true false true false false])
 assert (m(end), Inf);
 assert (v(end), Inf);
***** assert (nthargout (1:2, @binostat, 5, []), {[], []})
***** assert (nthargout (1:2, @binostat, [], 5), {[], []})
***** assert (size (binostat (randi (100, 10, 5, 4), rand (10, 5, 4))), [10 5 4])
***** assert (size (binostat (randi (100, 10, 5, 4), 7)), [10 5 4])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/tlsstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/tlsstat.m
***** error<tlsstat: function called with too few input arguments.> tlsstat ()
***** error<tlsstat: function called with too few input arguments.> tlsstat (1)
***** error<tlsstat: function called with too few input arguments.> tlsstat (1, 2)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat ({}, 2, 3)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, "", 3)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, 2, ["d"])
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (i, 2, 3)
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, i, 3)
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, 2, i)
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (ones (3), ones (2), 1)
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (ones (2), 1, ones (3))
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (1, ones (2), ones (3))
***** test
 [m, v] = tlsstat (0, 1, 0);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 2);
 assert (m, 0);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 1, 2);
 assert (m, 2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 1, 2);
 assert (m, -2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 2, 2);
 assert (m, 0);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 2, 2);
 assert (m, 2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 2, 2);
 assert (m, -2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 3);
 assert (m, 0);
 assert (v, 3);
***** test
 [m, v] = tlsstat (0, 2, 3);
 assert (m, 0);
 assert (v, 6);
***** test
 [m, v] = tlsstat (2, 1, 3);
 assert (m, 2);
 assert (v, 3);
***** test
 [m, v] = tlsstat (2, 2, 3);
 assert (m, 2);
 assert (v, 6);
***** test
 [m, v] = tlsstat (-2, 1, 3);
 assert (m, -2);
 assert (v, 3);
***** test
 [m, v] = tlsstat (-2, 2, 3);
 assert (m, -2);
 assert (v, 6);
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/dist_stat/betastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/betastat.m
***** error<betastat: function called with too few input arguments.> betastat ()
***** error<betastat: function called with too few input arguments.> betastat (1)
***** error<betastat: A and B must be numeric.> betastat ({}, 2)
***** error<betastat: A and B must be numeric.> betastat (1, "")
***** error<betastat: A and B must not be complex.> betastat (i, 2)
***** error<betastat: A and B must not be complex.> betastat (1, i)
***** error<betastat: A and B must be of common size or scalars.> ...
 betastat (ones (3), ones (2))
***** error<betastat: A and B must be of common size or scalars.> ...
 betastat (ones (2), ones (3))
***** test
 a = -2:6;
 b = 0.4:0.2:2;
 [m, v] = betastat (a, b);
 expected_m = [NaN NaN NaN 1/2 2/3.2 3/4.4 4/5.6 5/6.8 6/8];
 expected_v = [NaN NaN NaN 0.0833, 0.0558, 0.0402, 0.0309, 0.0250, 0.0208];
 assert (m, expected_m, eps*100);
 assert (v, expected_v, 0.001);
***** test
 a = -2:1:6;
 [m, v] = betastat (a, 1.5);
 expected_m = [NaN NaN NaN 1/2.5 2/3.5 3/4.5 4/5.5 5/6.5 6/7.5];
 expected_v = [NaN NaN NaN 0.0686, 0.0544, 0.0404, 0.0305, 0.0237, 0.0188];
 assert (m, expected_m);
 assert (v, expected_v, 0.001);
***** test
 a = [14  Inf   10  NaN  10];
 b = [12    9  NaN  Inf  12];
 [m, v] = betastat (a, b);
 expected_m = [14/26 NaN NaN NaN 10/22];
 expected_v = [168/18252 NaN NaN NaN 120/11132];
 assert (m, expected_m);
 assert (v, expected_v);
***** assert (nthargout (1:2, @betastat, 5, []), {[], []})
***** assert (nthargout (1:2, @betastat, [], 5), {[], []})
***** assert (size (betastat (rand (10, 5, 4), rand (10, 5, 4))), [10 5 4])
***** assert (size (betastat (rand (10, 5, 4), 7)), [10 5 4])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/lognstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/lognstat.m
***** error<lognstat: function called with too few input arguments.> lognstat ()
***** error<lognstat: function called with too few input arguments.> lognstat (1)
***** error<lognstat: MU and SIGMA must be numeric.> lognstat ({}, 2)
***** error<lognstat: MU and SIGMA must be numeric.> lognstat (1, "")
***** error<lognstat: MU and SIGMA must not be complex.> lognstat (i, 2)
***** error<lognstat: MU and SIGMA must not be complex.> lognstat (1, i)
***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
 lognstat (ones (3), ones (2))
***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
 lognstat (ones (2), ones (3))
***** test
 mu = 0:0.2:1;
 sigma = 0.2:0.2:1.2;
 [m, v] = lognstat (mu, sigma);
 expected_m = [1.0202, 1.3231, 1.7860, 2.5093,  3.6693,   5.5845];
 expected_v = [0.0425, 0.3038, 1.3823, 5.6447, 23.1345, 100.4437];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 sigma = 0.2:0.2:1.2;
 [m, v] = lognstat (0, sigma);
 expected_m = [1.0202, 1.0833, 1.1972, 1.3771, 1.6487,  2.0544];
 expected_v = [0.0425, 0.2036, 0.6211, 1.7002, 4.6708, 13.5936];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/logistat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/logistat.m
***** error<logistat: function called with too few input arguments.> logistat ()
***** error<logistat: function called with too few input arguments.> logistat (1)
***** error<logistat: MU and SIGMA must be numeric.> logistat ({}, 2)
***** error<logistat: MU and SIGMA must be numeric.> logistat (1, "")
***** error<logistat: MU and SIGMA must not be complex.> logistat (i, 2)
***** error<logistat: MU and SIGMA must not be complex.> logistat (1, i)
***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
 logistat (ones (3), ones (2))
***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
 logistat (ones (2), ones (3))
***** test
 [m, v] = logistat (0, 1);
 assert (m, 0);
 assert (v, 3.2899, 0.001);
***** test
 [m, v] = logistat (0, 0.8);
 assert (m, 0);
 assert (v, 2.1055, 0.001);
***** test
 [m, v] = logistat (1, 0.6);
 assert (m, 1);
 assert (v, 1.1844, 0.001);
***** test
 [m, v] = logistat (0, 0.4);
 assert (m, 0);
 assert (v, 0.5264, 0.001);
***** test
 [m, v] = logistat (-1, 0.2);
 assert (m, -1);
 assert (v, 0.1316, 0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/gevstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/gevstat.m
***** error<gevstat: function called with too few input arguments.> gevstat ()
***** error<gevstat: function called with too few input arguments.> gevstat (1)
***** error<gevstat: function called with too few input arguments.> gevstat (1, 2)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat ({}, 2, 3)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, "", 3)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, 2, "")
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (i, 2, 3)
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, i, 3)
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, 2, i)
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (ones (3), ones (2), 3)
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (ones (2), 2, ones (3))
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (1, ones (2), ones (3))
***** test
 k = [-1, -0.5, 0, 0.2, 0.4, 0.5, 1];
 sigma = 2;
 mu = 1;
 [m, v] = gevstat (k, sigma, mu);
 expected_m = [1, 1.4551, 2.1544, 2.6423, 3.4460, 4.0898, Inf];
 expected_v = [4, 3.4336, 6.5797, 13.3761, 59.3288, Inf, Inf];
 assert (m, expected_m, -0.001);
 assert (v, expected_v, -0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/tristat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/tristat.m
***** error<tristat: function called with too few input arguments.> tristat ()
***** error<tristat: function called with too few input arguments.> tristat (1)
***** error<tristat: function called with too few input arguments.> tristat (1, 2)
***** error<tristat: A, B, and C must be numeric.> tristat ("i", 2, 1)
***** error<tristat: A, B, and C must be numeric.> tristat (0, "d", 1)
***** error<tristat: A, B, and C must be numeric.> tristat (0, 3, {})
***** error<tristat: A, B, and C must be real.> tristat (i, 2, 1)
***** error<tristat: A, B, and C must be real.> tristat (0, i, 1)
***** error<tristat: A, B, and C must be real.> tristat (0, 3, i)
***** test
 a = 1:5;
 b = 3:7;
 c = 5:9;
 [m, v] = tristat (a, b, c);
 expected_m = [3, 4, 5, 6, 7];
 assert (m, expected_m);
 assert (v, ones (1, 5) * (2/3));
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/ncx2stat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/ncx2stat.m
***** error<ncx2stat: function called with too few input arguments.> ncx2stat ()
***** error<ncx2stat: function called with too few input arguments.> ncx2stat (1)
***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat ({}, 2)
***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat (1, "")
***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (i, 2)
***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (1, i)
***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
 ncx2stat (ones (3), ones (2))
***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
 ncx2stat (ones (2), ones (3))
***** shared df, d1
 df = [2, 0, -1, 1, 4];
 d1 = [1, NaN, 3, -1, 2];
***** assert (ncx2stat (df, d1), [3, NaN, NaN, NaN, 6]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 1), [3, NaN, 2, 5]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 3), [5, NaN, 4, 7]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 2), [4, NaN, 3, 6]);
***** assert (ncx2stat (2, [d1(1), d1(3:5)]), [3, 5, NaN, 4]);
***** assert (ncx2stat (0, [d1(1), d1(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (ncx2stat (1, [d1(1), d1(3:5)]), [2, 4, NaN, 3]);
***** assert (ncx2stat (4, [d1(1), d1(3:5)]), [5, 7, NaN, 6]);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/nctstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/nctstat.m
***** error<nctstat: function called with too few input arguments.> nctstat ()
***** error<nctstat: function called with too few input arguments.> nctstat (1)
***** error<nctstat: DF and MU must be numeric.> nctstat ({}, 2)
***** error<nctstat: DF and MU must be numeric.> nctstat (1, "")
***** error<nctstat: DF and MU must not be complex.> nctstat (i, 2)
***** error<nctstat: DF and MU must not be complex.> nctstat (1, i)
***** error<nctstat: DF and MU must be of common size or scalars.> ...
 nctstat (ones (3), ones (2))
***** error<nctstat: DF and MU must be of common size or scalars.> ...
 nctstat (ones (2), ones (3))
***** shared df, mu
 df = [2, 0, -1, 1, 4];
 mu = [1, NaN, 3, -1, 2];
***** assert (nctstat (df, mu), [1.7725, NaN, NaN, NaN, 2.5066], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 1), [1.7725, NaN, NaN, 1.2533], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 3), [5.3174, NaN, NaN, 3.7599], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 2), [3.5449, NaN, NaN, 2.5066], 1e-4);
***** assert (nctstat (2, [mu(1), mu(3:5)]), [1.7725,5.3174,-1.7725,3.5449], 1e-4);
***** assert (nctstat (0, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (nctstat (1, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (nctstat (4, [mu(1), mu(3:5)]), [1.2533,3.7599,-1.2533,2.5066], 1e-4);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/poisstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/poisstat.m
***** error<poisstat: function called with too few input arguments.> poisstat ()
***** error<poisstat: SIGMA must be numeric.> poisstat ({})
***** error<poisstat: SIGMA must be numeric.> poisstat ("")
***** error<poisstat: SIGMA must not be complex.> poisstat (i)
***** test
 lambda = 1 ./ (1:6);
 [m, v] = poisstat (lambda);
 assert (m, lambda);
 assert (v, lambda);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/unidstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/unidstat.m
***** error<unidstat: function called with too few input arguments.> unidstat ()
***** error<unidstat: N must be numeric.> unidstat ({})
***** error<unidstat: N must be numeric.> unidstat ("")
***** error<unidstat: N must not be complex.> unidstat (i)
***** test
 N = 1:6;
 [m, v] = unidstat (N);
 expected_m = [1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000];
 expected_v = [0.0000, 0.2500, 0.6667, 1.2500, 2.0000, 2.9167];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/loglstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/loglstat.m
***** error<loglstat: function called with too few input arguments.> loglstat ()
***** error<loglstat: function called with too few input arguments.> loglstat (1)
***** error<loglstat: MU and SIGMA must be numeric.> loglstat ({}, 2)
***** error<loglstat: MU and SIGMA must be numeric.> loglstat (1, "")
***** error<loglstat: MU and SIGMA must not be complex.> loglstat (i, 2)
***** error<loglstat: MU and SIGMA must not be complex.> loglstat (1, i)
***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
 loglstat (ones (3), ones (2))
***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
 loglstat (ones (2), ones (3))
***** test
 [m, v] = loglstat (0, 1);
 assert (m, Inf, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.8);
 assert (m, 4.2758, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.6);
 assert (m, 1.9820, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.4);
 assert (m, 1.3213, 0.001);
 assert (v, 2.5300, 0.001);
***** test
 [m, v] = loglstat (0, 0.2);
 assert (m, 1.0690, 0.001);
 assert (v, 0.1786, 0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/normstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/normstat.m
***** error<normstat: function called with too few input arguments.> normstat ()
***** error<normstat: function called with too few input arguments.> normstat (1)
***** error<normstat: MU and SIGMA must be numeric.> normstat ({}, 2)
***** error<normstat: MU and SIGMA must be numeric.> normstat (1, "")
***** error<normstat: MU and SIGMA must not be complex.> normstat (i, 2)
***** error<normstat: MU and SIGMA must not be complex.> normstat (1, i)
***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
 normstat (ones (3), ones (2))
***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
 normstat (ones (2), ones (3))
***** test
 mu = 1:6;
 sigma = 0.2:0.2:1.2;
 [m, v] = normstat (mu, sigma);
 expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
 assert (m, mu);
 assert (v, expected_v, 0.001);
***** test
 sigma = 0.2:0.2:1.2;
 [m, v] = normstat (0, sigma);
 expected_mn = [0, 0, 0, 0, 0, 0];
 expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/burrstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/burrstat.m
***** error<burrstat: function called with too few input arguments.> burrstat ()
***** error<burrstat: function called with too few input arguments.> burrstat (1)
***** error<burrstat: function called with too few input arguments.> burrstat (1, 2)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat ({}, 2, 3)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, "", 3)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, 2, "")
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (i, 2, 3)
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, i, 3)
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, 2, i)
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (ones (3), ones (2), 3)
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (ones (2), 2, ones (3))
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (1, ones (2), ones (3))
***** test
 [m, v] = burrstat (1, 2, 5);
 assert (m, 0.4295, 1e-4);
 assert (v, 0.0655, 1e-4);
***** test
 [m, v] = burrstat (1, 1, 1);
 assert (m, Inf);
 assert (v, Inf);
***** test
 [m, v] = burrstat (2, 4, 1);
 assert (m, 2.2214, 1e-4);
 assert (v, 1.3484, 1e-4);
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/hnstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/hnstat.m
***** error<hnstat: function called with too few input arguments.> hnstat ()
***** error<hnstat: function called with too few input arguments.> hnstat (1)
***** error<hnstat: MU and SIGMA must be numeric.> hnstat ({}, 2)
***** error<hnstat: MU and SIGMA must be numeric.> hnstat (1, "")
***** error<hnstat: MU and SIGMA must not be complex.> hnstat (i, 2)
***** error<hnstat: MU and SIGMA must not be complex.> hnstat (1, i)
***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
 hnstat (ones (3), ones (2))
***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
 hnstat (ones (2), ones (3))
***** test
 [m, v] = hnstat (0, 1);
 assert (m, 0.7979, 1e-4);
 assert (v, 0.3634, 1e-4);
***** test
 [m, v] = hnstat (2, 1);
 assert (m, 2.7979, 1e-4);
 assert (v, 0.3634, 1e-4);
***** test
 [m, v] = hnstat (2, 2);
 assert (m, 3.5958, 1e-4);
 assert (v, 1.4535, 1e-4);
***** test
 [m, v] = hnstat (2, 2.5);
 assert (m, 3.9947, 1e-4);
 assert (v, 2.2711, 1e-4);
***** test
 [m, v] = hnstat (1.5, 0.5);
 assert (m, 1.8989, 1e-4);
 assert (v, 0.0908, 1e-4);
***** test
 [m, v] = hnstat (-1.5, 0.5);
 assert (m, -1.1011, 1e-4);
 assert (v, 0.0908, 1e-4);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_stat/bisastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/bisastat.m
***** error<bisastat: function called with too few input arguments.> bisastat ()
***** error<bisastat: function called with too few input arguments.> bisastat (1)
***** error<bisastat: BETA and GAMMA must be numeric.> bisastat ({}, 2)
***** error<bisastat: BETA and GAMMA must be numeric.> bisastat (1, "")
***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (i, 2)
***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (1, i)
***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
 bisastat (ones (3), ones (2))
***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
 bisastat (ones (2), ones (3))
***** test
 beta = 1:6;
 gamma = 1:0.2:2;
 [m, v] = bisastat (beta, gamma);
 expected_m = [1.50, 3.44, 5.94,  9.12,  13.10, 18];
 expected_v = [2.25, 16.128, 60.858, 172.032, 409.050, 864];
 assert (m, expected_m, 1e-2);
 assert (v, expected_v, 1e-3);
***** test
 beta = 1:6;
 [m, v] = bisastat (beta, 1.5);
 expected_m = [2.125, 4.25, 6.375, 8.5, 10.625, 12.75];
 expected_v = [8.5781, 34.3125, 77.2031, 137.2500, 214.4531, 308.8125];
 assert (m, expected_m, 1e-3);
 assert (v, expected_v, 1e-4);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/gamstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/gamstat.m
***** error<gamstat: function called with too few input arguments.> gamstat ()
***** error<gamstat: function called with too few input arguments.> gamstat (1)
***** error<gamstat: A and B must be numeric.> gamstat ({}, 2)
***** error<gamstat: A and B must be numeric.> gamstat (1, "")
***** error<gamstat: A and B must not be complex.> gamstat (i, 2)
***** error<gamstat: A and B must not be complex.> gamstat (1, i)
***** error<gamstat: A and B must be of common size or scalars.> ...
 gamstat (ones (3), ones (2))
***** error<gamstat: A and B must be of common size or scalars.> ...
 gamstat (ones (2), ones (3))
***** test
 a = 1:6;
 b = 1:0.2:2;
 [m, v] = gamstat (a, b);
 expected_m = [1.00, 2.40, 4.20,  6.40,  9.00, 12.00];
 expected_v = [1.00, 2.88, 5.88, 10.24, 16.20, 24.00];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 a = 1:6;
 [m, v] = gamstat (a, 1.5);
 expected_m = [1.50, 3.00, 4.50, 6.00,  7.50,  9.00];
 expected_v = [2.25, 4.50, 6.75, 9.00, 11.25, 13.50];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/tstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/tstat.m
***** error<tstat: function called with too few input arguments.> tstat ()
***** error<tstat: DF must be numeric.> tstat ({})
***** error<tstat: DF must be numeric.> tstat ("")
***** error<tstat: DF must not be complex.> tstat (i)
***** test
 df = 3:8;
 [m, v] = tstat (df);
 expected_m = [0, 0, 0, 0, 0, 0];
 expected_v = [3.0000, 2.0000, 1.6667, 1.5000, 1.4000, 1.3333];
 assert (m, expected_m);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/evstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/evstat.m
***** error<evstat: function called with too few input arguments.> evstat ()
***** error<evstat: function called with too few input arguments.> evstat (1)
***** error<evstat: MU and SIGMA must be numeric.> evstat ({}, 2)
***** error<evstat: MU and SIGMA must be numeric.> evstat (1, "")
***** error<evstat: MU and SIGMA must not be complex.> evstat (i, 2)
***** error<evstat: MU and SIGMA must not be complex.> evstat (1, i)
***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
 evstat (ones (3), ones (2))
***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
 evstat (ones (2), ones (3))
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [NaN, NaN, 0.4228, 0.8456, 1.2684];
 y1 = [-5.5772, -3.4633, -3.0405, -2.6177, -2.1949];
***** assert (evstat (x, x), y0, 1e-4)
***** assert (evstat (x, x+6), y1, 1e-4)
***** assert (evstat (x, x-6), NaN (1,5))
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_stat/ncfstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/ncfstat.m
***** error<ncfstat: function called with too few input arguments.> ncfstat ()
***** error<ncfstat: function called with too few input arguments.> ncfstat (1)
***** error<ncfstat: function called with too few input arguments.> ncfstat (1, 2)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat ({}, 2, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, "", 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, 2, "")
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (i, 2, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, i, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, 2, i)
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (ones (3), ones (2), 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (ones (2), 2, ones (3))
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (1, ones (2), ones (3))
***** shared df1, df2, lambda
 df1 = [2, 0, -1, 1, 4, 5];
 df2 = [2, 4, -1, 5, 6, 7];
 lambda = [1, NaN, 3, 0, 2, -1];
***** assert (ncfstat (df1, df2, lambda), [NaN, NaN, NaN, 1.6667, 2.25, 1.12], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 1), [3.3333, 1.8750, 1.6800], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 2), [5.0000, 2.2500, 1.9600], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 3), [6.6667, 2.6250, 2.2400], 1e-4);
***** assert (ncfstat (2, [df2(1), df2(4:6)], 5), [NaN,5.8333,5.2500,4.9000], 1e-4);
***** assert (ncfstat (0, [df2(1), df2(4:6)], 5), [NaN, Inf, Inf, Inf]);
***** assert (ncfstat (1, [df2(1), df2(4:6)], 5), [NaN, 10, 9, 8.4], 1e-14);
***** assert (ncfstat (4, [df2(1), df2(4:6)], 5), [NaN, 3.75, 3.375, 3.15], 1e-14);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_stat/ricestat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/ricestat.m
***** error<ricestat: function called with too few input arguments.> ricestat ()
***** error<ricestat: function called with too few input arguments.> ricestat (1)
***** error<ricestat: S and SIGMA must be numeric.> ricestat ({}, 2)
***** error<ricestat: S and SIGMA must be numeric.> ricestat (1, "")
***** error<ricestat: S and SIGMA must not be complex.> ricestat (i, 2)
***** error<ricestat: S and SIGMA must not be complex.> ricestat (1, i)
***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
 ricestat (ones (3), ones (2))
***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
 ricestat (ones (2), ones (3))
***** shared s, sigma
 s = [2, 0, -1, 1, 4];
 sigma = [1, NaN, 3, -1, 2];
***** assert (ricestat (s, sigma), [2.2724, NaN, NaN, NaN, 4.5448], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 1), [2.2724, 1.2533, 1.5486, 4.1272], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 3), [4.1665, 3.7599, 3.8637, 5.2695], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 2), [3.0971, 2.5066, 2.6609, 4.5448], 1e-4);
***** assert (ricestat (2, [sigma(1), sigma(3:5)]), [2.2724, 4.1665, NaN, 3.0971], 1e-4);
***** assert (ricestat (0, [sigma(1), sigma(3:5)]), [1.2533, 3.7599, NaN, 2.5066], 1e-4);
***** assert (ricestat (1, [sigma(1), sigma(3:5)]), [1.5486, 3.8637, NaN, 2.6609], 1e-4);
***** assert (ricestat (4, [sigma(1), sigma(3:5)]), [4.1272, 5.2695, NaN, 4.5448], 1e-4);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/invgstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/invgstat.m
***** error<invgstat: function called with too few input arguments.> invgstat ()
***** error<invgstat: function called with too few input arguments.> invgstat (1)
***** error<invgstat: MU and LAMBDA must be numeric.> invgstat ({}, 2)
***** error<invgstat: MU and LAMBDA must be numeric.> invgstat (1, "")
***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (i, 2)
***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (1, i)
***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
 invgstat (ones (3), ones (2))
***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
 invgstat (ones (2), ones (3))
***** test
 [m, v] = invgstat (1, 1);
 assert (m, 1);
 assert (v, 1);
***** test
 [m, v] = invgstat (2, 1);
 assert (m, 2);
 assert (v, 8);
***** test
 [m, v] = invgstat (2, 2);
 assert (m, 2);
 assert (v, 4);
***** test
 [m, v] = invgstat (2, 2.5);
 assert (m, 2);
 assert (v, 3.2);
***** test
 [m, v] = invgstat (1.5, 0.5);
 assert (m, 1.5);
 assert (v, 6.75);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/wblstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/wblstat.m
***** error<wblstat: function called with too few input arguments.> wblstat ()
***** error<wblstat: function called with too few input arguments.> wblstat (1)
***** error<wblstat: LAMBDA and K must be numeric.> wblstat ({}, 2)
***** error<wblstat: LAMBDA and K must be numeric.> wblstat (1, "")
***** error<wblstat: LAMBDA and K must not be complex.> wblstat (i, 2)
***** error<wblstat: LAMBDA and K must not be complex.> wblstat (1, i)
***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
 wblstat (ones (3), ones (2))
***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
 wblstat (ones (2), ones (3))
***** test
 lambda = 3:8;
 k = 1:6;
 [m, v] = wblstat (lambda, k);
 expected_m = [3.0000, 3.5449, 4.4649, 5.4384, 6.4272, 7.4218];
 expected_v = [9.0000, 3.4336, 2.6333, 2.3278, 2.1673, 2.0682];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 k = 1:6;
 [m, v] = wblstat (6, k);
 expected_m = [ 6.0000, 5.3174, 5.3579, 5.4384, 5.5090, 5.5663];
 expected_v = [36.0000, 7.7257, 3.7920, 2.3278, 1.5923, 1.1634];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/geostat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/geostat.m
***** error<geostat: function called with too few input arguments.> geostat ()
***** error<geostat: PS must be numeric.> geostat ({})
***** error<geostat: PS must be numeric.> geostat ("")
***** error<geostat: PS must not be complex.> geostat (i)
***** test
 ps = 1 ./ (1:6);
 [m, v] = geostat (ps);
 assert (m, [0, 1, 2, 3, 4, 5], 0.001);
 assert (v, [0, 2, 6, 12, 20, 30], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/expstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/expstat.m
***** error<expstat: function called with too few input arguments.> expstat ()
***** error<expstat: MU must be numeric.> expstat ({})
***** error<expstat: MU must be numeric.> expstat ("")
***** error<expstat: MU must not be complex.> expstat (i)
***** test
 mu = 1:6;
 [m, v] = expstat (mu);
 assert (m, [1, 2, 3, 4, 5, 6], 0.001);
 assert (v, [1, 4, 9, 16, 25, 36], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/gpstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/gpstat.m
***** error<gpstat: function called with too few input arguments.> gpstat ()
***** error<gpstat: function called with too few input arguments.> gpstat (1)
***** error<gpstat: function called with too few input arguments.> gpstat (1, 2)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat ({}, 2, 3)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, "", 3)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, 2, "")
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (i, 2, 3)
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, i, 3)
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, 2, i)
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (ones (3), ones (2), 3)
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (ones (2), 2, ones (3))
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (1, ones (2), ones (3))
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0.5, 1, 2, Inf, Inf];
***** assert (gpstat (x, ones (1,6), zeros (1,6)), y, eps)
***** assert (gpstat (single (x), 1, 0), single (y), eps("single"))
***** assert (gpstat (x, single (1), 0), single (y), eps("single"))
***** assert (gpstat (x, 1, single (0)), single (y), eps("single"))
***** assert (gpstat (single ([x, NaN]), 1, 0), single ([y, NaN]), eps("single"))
***** assert (gpstat ([x, NaN], single (1), 0), single ([y, NaN]), eps("single"))
***** assert (gpstat ([x, NaN], 1, single (0)), single ([y, NaN]), eps("single"))
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_stat/chi2stat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/chi2stat.m
***** error<chi2stat: function called with too few input arguments.> chi2stat ()
***** error<chi2stat: DF must be numeric.> chi2stat ({})
***** error<chi2stat: DF must be numeric.> chi2stat ("")
***** error<chi2stat: DF must not be complex.> chi2stat (i)
***** test
 df = 1:6;
 [m, v] = chi2stat (df);
 assert (m, df);
 assert (v, [2, 4, 6, 8, 10, 12], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/hygestat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/hygestat.m
***** error<hygestat: function called with too few input arguments.> hygestat ()
***** error<hygestat: function called with too few input arguments.> hygestat (1)
***** error<hygestat: function called with too few input arguments.> hygestat (1, 2)
***** error<hygestat: M, K, and N must be numeric.> hygestat ({}, 2, 3)
***** error<hygestat: M, K, and N must be numeric.> hygestat (1, "", 3)
***** error<hygestat: M, K, and N must be numeric.> hygestat (1, 2, "")
***** error<hygestat: M, K, and N must not be complex.> hygestat (i, 2, 3)
***** error<hygestat: M, K, and N must not be complex.> hygestat (1, i, 3)
***** error<hygestat: M, K, and N must not be complex.> hygestat (1, 2, i)
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (ones (3), ones (2), 3)
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (ones (2), 2, ones (3))
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (1, ones (2), ones (3))
***** test
 m = 4:9;
 k = 0:5;
 n = 1:6;
 [mn, v] = hygestat (m, k, n);
 expected_mn = [0.0000, 0.4000, 1.0000, 1.7143, 2.5000, 3.3333];
 expected_v = [0.0000, 0.2400, 0.4000, 0.4898, 0.5357, 0.5556];
 assert (mn, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
***** test
 m = 4:9;
 k = 0:5;
 [mn, v] = hygestat (m, k, 2);
 expected_mn = [0.0000, 0.4000, 0.6667, 0.8571, 1.0000, 1.1111];
 expected_v = [0.0000, 0.2400, 0.3556, 0.4082, 0.4286, 0.4321];
 assert (mn, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_stat/fstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/fstat.m
***** error<fstat: function called with too few input arguments.> fstat ()
***** error<fstat: function called with too few input arguments.> fstat (1)
***** error<fstat: DF1 and DF2 must be numeric.> fstat ({}, 2)
***** error<fstat: DF1 and DF2 must be numeric.> fstat (1, "")
***** error<fstat: DF1 and DF2 must not be complex.> fstat (i, 2)
***** error<fstat: DF1 and DF2 must not be complex.> fstat (1, i)
***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
 fstat (ones (3), ones (2))
***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
 fstat (ones (2), ones (3))
***** test
 df1 = 1:6;
 df2 = 5:10;
 [m, v] = fstat (df1, df2);
 expected_mn = [1.6667, 1.5000, 1.4000, 1.3333, 1.2857, 1.2500];
 expected_v = [22.2222, 6.7500, 3.4844, 2.2222, 1.5869, 1.2153];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
***** test
 df1 = 1:6;
 [m, v] = fstat (df1, 5);
 expected_mn = [1.6667, 1.6667, 1.6667, 1.6667, 1.6667, 1.6667];
 expected_v = [22.2222, 13.8889, 11.1111, 9.7222, 8.8889, 8.3333];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/raylstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/raylstat.m
***** error<raylstat: function called with too few input arguments.> raylstat ()
***** error<raylstat: SIGMA must be numeric.> raylstat ({})
***** error<raylstat: SIGMA must be numeric.> raylstat ("")
***** error<raylstat: SIGMA must not be complex.> raylstat (i)
***** test
 sigma = 1:6;
 [m, v] = raylstat (sigma);
 expected_m = [1.2533, 2.5066, 3.7599, 5.0133, 6.2666, 7.5199];
 expected_v = [0.4292, 1.7168, 3.8628, 6.8673, 10.7301, 15.4513];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/nbinstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/nbinstat.m
***** error<nbinstat: function called with too few input arguments.> nbinstat ()
***** error<nbinstat: function called with too few input arguments.> nbinstat (1)
***** error<nbinstat: R and PS must be numeric.> nbinstat ({}, 2)
***** error<nbinstat: R and PS must be numeric.> nbinstat (1, "")
***** error<nbinstat: R and PS must not be complex.> nbinstat (i, 2)
***** error<nbinstat: R and PS must not be complex.> nbinstat (1, i)
***** error<nbinstat: R and PS must be of common size or scalars.> ...
 nbinstat (ones (3), ones (2))
***** error<nbinstat: R and PS must be of common size or scalars.> ...
 nbinstat (ones (2), ones (3))
***** test
 r = 1:4;
 ps = 0.2:0.2:0.8;
 [m, v] = nbinstat (r, ps);
 expected_m = [ 4.0000, 3.0000, 2.0000, 1.0000];
 expected_v = [20.0000, 7.5000, 3.3333, 1.2500];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 r = 1:4;
 [m, v] = nbinstat (r, 0.5);
 expected_m = [1, 2, 3, 4];
 expected_v = [2, 4, 6, 8];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/unifstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/unifstat.m
***** error<unifstat: function called with too few input arguments.> unifstat ()
***** error<unifstat: function called with too few input arguments.> unifstat (1)
***** error<unifstat: A and B must be numeric.> unifstat ({}, 2)
***** error<unifstat: A and B must be numeric.> unifstat (1, "")
***** error<unifstat: A and B must not be complex.> unifstat (i, 2)
***** error<unifstat: A and B must not be complex.> unifstat (1, i)
***** error<unifstat: A and B must be of common size or scalars.> ...
 unifstat (ones (3), ones (2))
***** error<unifstat: A and B must be of common size or scalars.> ...
 unifstat (ones (2), ones (3))
***** test
 a = 1:6;
 b = 2:2:12;
 [m, v] = unifstat (a, b);
 expected_m = [1.5000, 3.0000, 4.5000, 6.0000, 7.5000, 9.0000];
 expected_v = [0.0833, 0.3333, 0.7500, 1.3333, 2.0833, 3.0000];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 a = 1:6;
 [m, v] = unifstat (a, 10);
 expected_m = [5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000];
 expected_v = [6.7500, 5.3333, 4.0833, 3.0000, 2.0833, 1.3333];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/nakastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/nakastat.m
***** error<nakastat: function called with too few input arguments.> nakastat ()
***** error<nakastat: function called with too few input arguments.> nakastat (1)
***** error<nakastat: MU and OMEGA must be numeric.> nakastat ({}, 2)
***** error<nakastat: MU and OMEGA must be numeric.> nakastat (1, "")
***** error<nakastat: MU and OMEGA must not be complex.> nakastat (i, 2)
***** error<nakastat: MU and OMEGA must not be complex.> nakastat (1, i)
***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
 nakastat (ones (3), ones (2))
***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
 nakastat (ones (2), ones (3))
***** test
 [m, v] = nakastat (1, 1);
 assert (m, 0.8862269254, 1e-10);
 assert (v, 0.2146018366, 1e-10);
***** test
 [m, v] = nakastat (1, 2);
 assert (m, 1.25331413731, 1e-10);
 assert (v, 0.42920367321, 1e-10);
***** test
 [m, v] = nakastat (2, 1);
 assert (m, 0.93998560299, 1e-10);
 assert (v, 0.11642706618, 1e-10);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/isoutlier.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/isoutlier.m
***** demo
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 TF = isoutlier (A, "mean")
***** demo
 ## Use a moving detection method to detect local outliers in a sine wave

 x = -2*pi:0.1:2*pi;
 A = sin(x);
 A(47) = 0;
 time = datenum (2023,1,1,0,0,0) + (1/24)*[0:length(x)-1] - 730485;
 TF = isoutlier (A, "movmedian", 5*(1/24), "SamplePoints", time);
 plot (time, A)
 hold on
 plot (time(TF), A(TF), "x")
 datetick ('x', 20, 'keepticks')
 legend ("Original Data", "Outlier Data")
***** demo
 ## Locate an outlier in a vector of data and visualize the outlier

 x = 1:10;
 A = [60 59 49 49 58 100 61 57 48 58];
 [TF, L, U, C] = isoutlier (A);
 plot (x, A);
 hold on
 plot (x(TF), A(TF), "x");
 xlim ([1,10]);
 line ([1,10], [L, L], "Linestyle", ":");
 text (1.1, L-2, "Lower Threshold");
 line ([1,10], [U, U], "Linestyle", ":");
 text (1.1, U-2, "Upper Threshold");
 line ([1,10], [C, C], "Linestyle", ":");
 text (1.1, C-3, "Center Value");
 legend ("Original Data", "Outlier Data");
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 assert (isoutlier (A, "mean"), logical([zeros(1,8) 1 zeros(1,6)]))
 assert (isoutlier (A, "median"), ...
 logical([zeros(1,3) 1 zeros(1,4) 1 zeros(1,6)]))
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "mean");
 assert (L, -109.2459044922864, 1e-12)
 assert (U, 264.9792378256198, 1e-12)
 assert (C, 77.8666666666666, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "median");
 assert (L, 50.104386688966386, 1e-12)
 assert (U, 67.895613311033610, 1e-12)
 assert (C, 59)
***** test
 A = magic(5) + diag(200*ones(1,5));
 T = logical (eye (5));
 assert (isoutlier (A, 2), T)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmedian", 5);
 l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
      53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
 u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
      62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
 c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmedian", 5, "SamplePoints", [1:15]);
 l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
      53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
 u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
      62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
 c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmean", 5);
 l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
      -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
      52.5979, 51.0627];
 u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
      430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
      66.9373];
 c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
      60.6, 59.8, 59.25, 59];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c, 1e-4)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmean", 5, "SamplePoints", [1:15]);
 l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
      -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
      52.5979, 51.0627];
 u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
      430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
      66.9373];
 c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
      60.6, 59.8, 59.25, 59];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c, 1e-4)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd");
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 34.235977035439944, 1e-12)
 assert (U, 89.764022964560060, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 0.01);
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 31.489256770616173, 1e-12)
 assert (U, 92.510743229383820, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 5e-10);
 assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 23.976664158788935, 1e-12)
 assert (U, 100.02333584121110, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "grubbs");
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 54.642809574646606, 1e-12)
 assert (U, 63.511036579199555, 1e-12)
 assert (C, 59.076923076923080, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "grubbs", "ThresholdFactor", 0.01);
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 54.216083184201850, 1e-12)
 assert (U, 63.937762969644310, 1e-12)
 assert (C, 59.076923076923080, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A,  "percentiles", [10 90]);
 assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 57)
 assert (U, 100)
 assert (C, 78.5)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A,  "percentiles", [20 80]);
 assert (TF, logical ([1 0 0 1 0 0 1 0 1 0 0 0 0 0 1]))
 assert (L, 57.5)
 assert (U, 62)
 assert (C, 59.75)
***** shared A
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", 0);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", []);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [2 3 4]);
***** error<isoutlier: WINDOW must be a positive integer> ...
 isoutlier (A, "movmedian", 1.4);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [0 1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [2 -1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", {2 3});
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", "char");

***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", 0);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", []);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [2 3 4]);
***** error<isoutlier: WINDOW must be a positive integer> ...
 isoutlier (A, "movmean", 1.4);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [0 1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [2 -1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", {2 3});
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", "char");

***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [-1 90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [10 -90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90 20]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90 20]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [10 20 90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", {10 90});
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", "char");

***** error<isoutlier: sample points must be a vector.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", ones(3,15));
***** error<isoutlier: sample points must be a vector.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", 15);
***** error<isoutlier: sample points must be unique.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [1,1:14]);
***** error<isoutlier: sample points must be sorted.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [2,1,3:15]);
***** error<isoutlier: sample points must have the same size> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [1:14]);

***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
 isoutlier (A, "movmean", 5, "ThresholdFactor", [1:14]);
***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
 isoutlier (A, "movmean", 5, "ThresholdFactor", -1);
***** error<isoutlier: threshold factor must must be in> ...
 isoutlier (A, "gesd", "ThresholdFactor", 3);
***** error<isoutlier: threshold factor must must be in> ...
 isoutlier (A, "grubbs", "ThresholdFactor", 3);

***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", [1:14]);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", -1);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", 0);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", 1.5);

***** error<isoutlier: invalid input argument.> ...
 isoutlier (A, {"movmean"}, 5, "SamplePoints", [1:15]);
***** error<isoutlier: invalid input argument.> isoutlier (A, {1});
***** error<isoutlier: invalid input argument.> isoutlier (A, true);
***** error<isoutlier: invalid input argument.> isoutlier (A, false);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, 0);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, [1 2]);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, -2);
59 tests, 59 passed, 0 known failure, 0 skipped
[inst/fishertest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fishertest.m
***** demo
 ## A Fisher's exact test example

 x = [3, 1; 1, 3]
 [h, p, stats] = fishertest(x)
***** assert (fishertest ([3, 4; 5, 7]), false);
***** assert (isa (fishertest ([3, 4; 5, 7]), "logical"), true);
***** test
 [h, pval, stats] = fishertest ([3, 4; 5, 7]);
 assert (pval, 1, 1e-14);
 assert (stats.OddsRatio, 1.05);
 CI = [0.159222057151289, 6.92429189601808];
 assert (stats.ConfidenceInterval, CI, 1e-14)
***** test
 [h, pval, stats] = fishertest ([3, 4; 5, 0]);
 assert (pval, 0.08080808080808080, 1e-14);
 assert (stats.OddsRatio, 0);
 assert (stats.ConfidenceInterval, [-Inf, Inf])
***** error fishertest ();
***** error fishertest (1, 2, 3, 4, 5, 6);
***** error<fishertest: X must be a 2-dimensional matrix.> ...
 fishertest (ones (2, 2, 2));
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; -3, 4]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; 3, 4+i]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; 3, 4.2]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([NaN, 2; 3, 4]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, Inf; 3, 4]);
***** error<fishertest: cannot handle large entries> ...
 fishertest (ones (2) * 1e8);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", 0);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", 1.2);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", "val");
***** error<fishertest: invalid value for tail.>  ...
 fishertest ([1, 2; 3, 4], "tail", "val");
***** error<fishertest: invalid value for tail.>  ...
 fishertest ([1, 2; 3, 4], "alpha", 0.01, "tail", "val");
***** error<fishertest: invalid name for optional arguments.> ...
 fishertest ([1, 2; 3, 4], "alpha", 0.01, "badoption", 3);
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/normplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/normplot.m
***** demo
 h = normplot([1:20]);
***** demo
 h = normplot([1:20;5:2:44]');
***** demo
 ax = newplot();
 h = normplot(ax, [1:20]);
 ax = gca;
 h = normplot(ax, [-10:10]);
 set (ax, "xlim", [-11, 21]);
***** error normplot ();
***** error normplot (23);
***** error normplot (23, [1:20]);
***** error normplot (ones(3,4,5));
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = newplot (hf);
   h = normplot (ax, [1:20]);
   ax = gca;
   h = normplot(ax, [-10:10]);
   set (ax, "xlim", [-11, 21]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   h = normplot([1:20;5:2:44]');
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/fitcknn.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcknn.m
***** demo
 ## Train a k-nearest neighbor classifier for k = 10
 ## and plot the decision boundaries.

 load fisheriris
 idx = ! strcmp (species, "setosa");
 X = meas(idx,3:4);
 Y = cast (strcmpi (species(idx), "virginica"), "double");
 obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
 x1 = [min(X(:,1)):0.03:max(X(:,1))];
 x2 = [min(X(:,2)):0.02:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);
 gidx = logical (str2num (cell2mat (pred)));

 figure
 scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel ("Petal length (cm)");
 ylabel ("Petal width (cm)");
 title ("5-Nearest Neighbor Classifier Decision Boundary");
 legend ({"Versicolor Region", "Virginica Region", ...
         "Sampled Versicolor", "Sampled Virginica"}, ...
         "location", "northwest")
 axis tight
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = ones (4, 11);
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k, "Distance", "hamming");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = fitcknn (x, y, "Standardize", 1);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {true})
 assert ({a.Sigma}, {std(x, [], 1)})
 assert ({a.Mu}, {[3.75, 4.25, 4.75]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = fitcknn (x, y, "Standardize", false);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {false})
 assert ({a.Sigma}, {[]})
 assert ({a.Mu}, {[]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 s = ones (1, 3);
 a = fitcknn (x, y, "Scale" , s, "Distance", "seuclidean");
 assert (class (a), "ClassificationKNN");
 assert ({a.DistParameter}, {s})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski", ...
                    "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "BucketSize" , 20, "distance", "mahalanobis");
 assert (class (a), "ClassificationKNN");
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
 assert ({a.BucketSize}, {20})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "IncludeTies", true);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, true);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, false);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, [0.5; 0.5])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 prior = [0.5; 0.5];
 a = fitcknn (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.75; 0.25];
 a = fitcknn (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.5; 0.5];
 a = fitcknn (x, y, "Prior", "uniform");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = fitcknn (x, y, "Cost", cost);
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = fitcknn (x, y, "Cost", cost, "Distance", "hamming" );
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "NSMethod", "exhaustive", "CrossVal", "on");
 assert (class (a), "ClassificationPartitionedModel");
 assert ({a.X, a.Y, a.Trained{1}.NumNeighbors}, {x, y, 1})
 assert (a.ModelParameters.NSMethod, "exhaustive")
 assert (a.ModelParameters.Distance, "euclidean")
 assert ({a.Trained{1}.BucketSize}, {50})
***** error<fitcknn: too few arguments.> fitcknn ()
***** error<fitcknn: too few arguments.> fitcknn (ones (4,1))
***** error<fitcknn: Name-Value arguments must be in pairs.>
 fitcknn (ones (4,2), ones (4, 1), "K")
***** error<fitcknn: number of rows in X and Y must be equal.>
 fitcknn (ones (4,2), ones (3, 1))
***** error<fitcknn: number of rows in X and Y must be equal.>
 fitcknn (ones (4,2), ones (3, 1), "K", 2)
***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
 fitcknn (ones (4,2), ones (4, 1), "CrossVal", 2)
***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
 fitcknn (ones (4,2), ones (4, 1), "CrossVal", 'a')
***** error <fitcknn: You can use only one cross-validation name-value pair argument> ...
 fitcknn (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/harmmean.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/harmmean.m
***** test
 x = [0:10];
 y = [x;x+5;x+10];
 assert (harmmean (x), 0);
 m = [0 8.907635160795225 14.30854471766802];
 assert (harmmean (y, 2), m', 4e-14);
 assert (harmmean (y, "all"), 0);
 y(2,4) = NaN;
 m(2) = 9.009855936313949;
 assert (harmmean (y, 2), [0 NaN m(3)]', 4e-14);
 assert (harmmean (y', "omitnan"), m, 4e-14);
 z = y + 20;
 assert (harmmean (z, "all"), NaN);
 assert (harmmean (z, "all", "includenan"), NaN);
 assert (harmmean (z, "all", "omitnan"), 29.1108719858295, 4e-14);
 m = [24.59488458841874 NaN 34.71244385944397];
 assert (harmmean (z'), m, 4e-14);
 assert (harmmean (z', "includenan"), m, 4e-14);
 m(2) = 29.84104075528277;
 assert (harmmean (z', "omitnan"), m, 4e-14);
 assert (harmmean (z, 2, "omitnan"), m', 4e-14);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (harmmean (x, [3 2])), [10 1 1 3]);
 assert (size (harmmean (x, [1 2])), [1 1 6 3]);
 assert (size (harmmean (x, [1 2 4])), [1 1 6]);
 assert (size (harmmean (x, [1 4 3])), [1 40]);
 assert (size (harmmean (x, [1 2 3 4])), [1 1]);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 m = repmat ([5.559045930488016;13.04950789021461], [5 1 1 3]);
 assert (harmmean (x, [3 2]), m, 4e-14);
 x(2,5,6,3) = NaN;
 m(2,3) = NaN;
 assert (harmmean (x, [3 2]), m, 4e-14);
 m(2,3) = 13.06617961315406;
 assert (harmmean (x, [3 2], "omitnan"), m, 4e-14);
***** error <harmmean: X must contain real nonnegative values.> harmmean ("char")
***** error <harmmean: X must contain real nonnegative values.> harmmean ([1 -1 3])
***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
***** error <harmmean: VECDIM must contain non-repeating positive integers.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/cophenet.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cophenet.m
***** demo
 randn ("seed", 5)  # for reproducibility
 X = randn (10,2);
 y = pdist (X);
 Z = linkage (y, "average");
 cophenet (Z, y)
***** error<cophenet: function called with too few input arguments.> cophenet ()
***** error<cophenet: function called with too few input arguments.> cophenet (1)
***** error<cophenet: Z must be a matrix as generated by the linkage function.> ...
 cophenet (ones (2,2), 1)
***** error<cophenet: Y must be a vector of euclidean distances.> ...
 cophenet ([1 2 1], "a")
***** error<cophenet: Y must be a vector of euclidean distances.> ...
 cophenet ([1 2 1], [1 2])
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/ztest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ztest2.m
***** error ztest2 ();
***** error ztest2 (1);
***** error ztest2 (1, 2);
***** error ztest2 (1, 2, 3);
***** error<ztest2: optional arguments must be in NAME-VALUE pairs.> ...
 ztest2 (1, 2, 3, 4, "alpha")
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", 0);
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", 1.2);
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", "val");
***** error<ztest2: invalid VALUE for tail.>  ...
 ztest2 (1, 2, 3, 4, "tail", "val");
***** error<ztest2: invalid VALUE for tail.>  ...
 ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "val");
***** error<ztest2: invalid NAME for optional arguments.> ...
 ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "both", "badoption", 3);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/chi2gof.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/chi2gof.m
***** demo
 x = normrnd (50, 5, 100, 1);
 [h, p, stats] = chi2gof (x)
 [h, p, stats] = chi2gof (x, "cdf", @(x)normcdf (x, mean(x), std(x)))
 [h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)})
***** demo
 x = rand (100,1 );
 n = length (x);
 binedges = linspace (0, 1, 11);
 expectedCounts = n * diff (binedges);
 [h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts)
***** demo
 bins = 0:5;
 obsCounts = [6 16 10 12 4 2];
 n = sum(obsCounts);
 lambdaHat = sum(bins.*obsCounts) / n;
 expCounts = n * poisspdf(bins,lambdaHat);
 [h, p, stats] = chi2gof (bins, "binctrs", bins, "frequency", obsCounts, ...
                          "expected", expCounts, "nparams",1)
***** error chi2gof ()
***** error chi2gof ([2,3;3,4])
***** error chi2gof ([1,2,3,4], "nbins", 3, "ctrs", [2,3,4])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,-2])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "nparams", i)
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "alpha", 1.3)
***** error chi2gof ([1,2,3,4], "expected", [-3,2,2])
***** error chi2gof ([1,2,3,4], "expected", [3,2,2], "nbins", 5)
***** error chi2gof ([1,2,3,4], "cdf", @normcdff)
***** test
 x = [1 2 1 3 2 4 3 2 4 3 2 2];
 [h, p, stats] = chi2gof (x);
 assert (h, 0);
 assert (p, NaN);
 assert (stats.chi2stat, 0.1205375022748029, 1e-14);
 assert (stats.df, 0);
 assert (stats.edges, [1, 2.5, 4], 1e-14);
 assert (stats.O, [7, 5], 1e-14);
 assert (stats.E, [6.399995519909668, 5.600004480090332], 1e-14);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/bar3h.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/bar3h.m
***** demo
 ## Ploting 5 bars in the same series.

 y = [50; 40; 30; 20; 10];
 bar3h (y);
***** demo
 ## Ploting 5 bars in different groups.

 y = [50, 40, 30, 20, 10];
 bar3h (y);
***** demo
 ## A 3D bar graph with each series corresponding to a column in y.

 y = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
 bar3h (y);
***** demo
 ## Specify z-axis locations as tick names. z must be a column vector!

 z = [1950, 1960, 1970, 1980, 1990]';
 y = [16, 8, 4, 2, 1]';
 bar3h (z, y);
***** demo
 ## Plot 3 series as a grouped plot without any space between the grouped bars

 y = [70 50 33 10; 75 55 35 15; 80 60 40 20];
 bar3h (y, 1, 'grouped');
***** demo
 ## Plot a stacked style 3D bar graph

 y = [19, 30, 21, 30; 40, 16, 32, 12];
 b = bar3h (y, 0.5, 'stacked');
***** error <bar3h: Z must be numeric.> bar3h ("A")
***** error <bar3h: Z must be numeric.> bar3h ({2,3,4,5})
***** error <bar3h: inconsistent size in Y and Z input arguments.> ...
 bar3h ([1,2,3]', ones (2))
***** error <bar3h: WIDTH must be a scalar in the range> ...
 bar3h ([1:5], 1.2)
***** error <bar3h: WIDTH must be a scalar in the range> ...
 bar3h ([1:5]', ones (5), 1.2)
***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3h ([1:5]', ones (5), [0.8, 0.7])
***** error <bar3h: missing value for optional argument 'width'.> ...
 bar3h (ones (5), 'width')
***** error <bar3h: invalid value for optional argument 'width'.> ...
 bar3h (ones (5), 'width', 1.2)
***** error <bar3h: invalid value for optional argument 'width'.> ...
 bar3h (ones (5), 'width', [0.8, 0.8, 0.8])
***** error <bar3h: missing value for optional argument 'color'.> ...
 bar3h (ones (5), 'color')
***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3h (ones (5), 'color', [0.8, 0.8])
***** error <bar3h: invalid value for optional argument 'color'.> ...
 bar3h (ones (5), 'color', "brown")
***** error <bar3h: invalid value for optional argument 'color'.> ...
 bar3h (ones (5), 'color', {"r", "k", "c", "m", "brown"})
***** error <bar3h: missing value for optional argument 'xlabel'.> ...
 bar3h (ones (5), 'xlabel')
***** error <bar3h: invalid value for optional argument 'xlabel'.> ...
 bar3h (ones (5), 'xlabel', 4)
***** error <bar3h: missing value for optional argument 'zlabel'.> ...
 bar3h (ones (5), 'zlabel')
***** error <bar3h: invalid value for optional argument 'zlabel'.> ...
 bar3h (ones (5), 'zlabel', 4)
***** error <bar3h: invalid optional argument.> bar3h (ones (5), 'this', 4)
***** error <bar3h: the elements in 'xlabel' must equal the columns in Z.> ...
 bar3h (ones (5), 'xlabel', {"A", "B", "C"})
***** error <bar3h: the elements in 'zlabel' must equal the rows in Z.> ...
 bar3h (ones (5), 'zlabel', {"A", "B", "C"})
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/datasample.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/datasample.m
***** error datasample();
***** error datasample(1);
***** error <data must be a vector or matrix> datasample({1, 2, 3}, 1);
***** error <k must be a positive integer scalar> datasample([1 2], -1);
***** error <k must be a positive integer scalar> datasample([1 2], 1.5);
***** error <k must be a positive integer scalar> datasample([1 2], [1 1]);
***** error <k must be a positive integer scalar> datasample([1 2], 'g', [1 1]);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, -1);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, 1.5);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, [1 1]);
***** error <Replace> datasample([1 2], 1, 1, "Replace", -2);
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", "abc");
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", [1 -2 3]);
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", ones (2));
***** error <weights must be equal> datasample([1 2], 1, 1, "Weights", [1 2 3]);
***** test
 dat = randn (10, 4);
 assert (size (datasample (dat, 3, 1)), [3 4]);
***** test
 dat = randn (10, 4);
 assert (size (datasample (dat, 3, 2)), [10 3]);
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/cluster.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cluster.m
***** error cluster ()
***** error <Z must be .* generated by the linkage .*> cluster ([1 1], "Cutoff", 1)
***** error <unknown option .*> cluster ([1 2 1], "Bogus", 1)
***** error <C must be a positive scalar .*> cluster ([1 2 1], "Cutoff", -1)
***** error <unknown property .*> cluster ([1 2 1], "Cutoff", 1, "Bogus", 1)
***** test
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/sampsizepwr.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/sampsizepwr.m
***** demo
 ## Compute the mean closest to 100 that can be determined to be
 ## significantly different from 100 using a t-test with a sample size
 ## of 60 and a power of 0.8.
 mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
 disp (mu1);
***** demo
 ## Compute the sample sizes required to distinguish mu0 = 100 from
 ## mu1 = 110 by a two-sample t-test with a ratio of the larger and the
 ## smaller sample sizes of 1.5 and a power of 0.6.
 [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5)
***** demo
 ## Compute the sample size N required to distinguish p=.26 from p=.2
 ## with a binomial test.  The result is approximate, so make a plot to
 ## see if any smaller N values also have the required power of 0.6.
 Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
 nn = 1:250;
 pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
 Nexact = min (nn(pwr >= 0.6));
 plot(nn,pwr,'b-', [Napprox Nexact],pwr([Napprox Nexact]),'ro');
 grid on
***** demo
 ## The company must test 52 bottles to detect the difference between a mean
 ## volume of 100 mL and 102 mL with a power of 0.80.  Generate a power curve
 ## to visualize how the sample size affects the power of the test.

 nout = sampsizepwr('t',[100 5],102,0.80);
 nn = 1:100;
 pwrout = sampsizepwr('t',[100 5],102,[],nn);

 figure;
 plot (nn, pwrout, "b-", nout, 0.8, "ro")
 title ("Power versus Sample Size")
 xlabel ("Sample Size")
 ylabel ("Power")
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr ([], [100, 10], [], 0.8, 60);
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr (3, [100, 10], [], 0.8, 60);
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr ({"t", "t2"}, [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid test type.> ...
 out = sampsizepwr ("reg", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: parameters must be numeric.> ...
 out = sampsizepwr ("t", ["a", "e"], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("z", 100, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("t", 100, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("t2", 60, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("var", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("p", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("r", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("z", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("t", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("var", 2, [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("p", 0.1, [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("r", 0.5, [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("z", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("z", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("t", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("t", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 [out, N1] = sampsizepwr ("t2", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 [out, N1] = sampsizepwr ("t2", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("var", 0, [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("var", -5, [], 0.8, 60);
***** error<sampsizepwr: out of range probability.> ...
 out = sampsizepwr ("p", 0, [], 0.8, 60);
***** error<sampsizepwr: out of range probability.> ...
 out = sampsizepwr ("p", 1.2, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", -1.5, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", -1, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", 1.2, [], 0.8, 60);
***** error<sampsizepwr: regression coefficient must not be 0.> ...
 out = sampsizepwr ("r", 0, [], 0.8, 60);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", -0.2);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 0);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 1.5);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", "zero");
***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", 1.5);
***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", {"both", "left"});
***** error<sampsizepwr: invalid value for 'tail' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", "other");
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", "some");
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", 0.5);
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", [2, 1.3, 0.3]);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], [], [], 60);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], 110, [], []);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], [], 0.8, []);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], 110, 0.8, 60);
***** error<sampsizepwr: alternative hypothesis parameter must be numeric.> ...
 out = sampsizepwr ("z", [100, 5], "mu", [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("var", 5, -1, [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("p", 0.8, 1.2, [], 60, "tail", "right");
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("r", 0.8, 1.2, [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("r", 0.8, -1.2, [], 60);
***** error<sampsizepwr: invalid value for POWER.> ...
 out = sampsizepwr ("z", [100, 5], 110, 1.2);
***** error<sampsizepwr: invalid value for POWER.> ...
 out = sampsizepwr ("z", [100, 5], 110, 0);
***** error<sampsizepwr: Cannot compute N or P1 unless POWER> ...
 out = sampsizepwr ("z", [100, 5], 110, 0.05, [], "alpha", 0.1);
***** error<sampsizepwr: input arguments size mismatch.> ...
 out = sampsizepwr ("z", [100, 5], [], [0.8, 0.7], [60, 80, 100]);
***** error<sampsizepwr: Same value for null and alternative hypothesis.> ...
 out = sampsizepwr ("t", [100, 5], 100, 0.8, []);
***** error<sampsizepwr: Invalid P1 for testing left tail.> ...
 out = sampsizepwr ("t", [100, 5], 110, 0.8, [], "tail", "left");
***** error<sampsizepwr: Invalid P1 for testing right tail.> ...
 out = sampsizepwr ("t", [100, 5], 90, 0.8, [], "tail", "right");
***** warning<sampsizepwr: approximate N.> ...
 Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
***** warning<sampsizepwr: approximate N.> ...
 Napprox = sampsizepwr ("p", 0.30, 0.36, 0.8);
***** test
 mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
 assert (mu1, 103.67704316, 1e-8);
***** test
 [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5);
 assert (N1, 9);
 assert (N2, 14);
***** test
 nn = 1:250;
 pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
 pwr_out = [0, 0.0676, 0.0176, 0.0566, 0.0181, 0.0431, 0.0802, 0.0322];
 assert (pwr([1:8]), pwr_out, 1e-4 * ones (1,8));
 pwr_out = [0.59275, 0.6073, 0.62166, 0.6358, 0.6497, 0.6087, 0.6229, 0.6369];
 assert (pwr([243:end]), pwr_out, 1e-4 * ones (1,8));
***** test
 nout = sampsizepwr ("t", [100, 5], 102, 0.80);
 assert (nout, 52);
***** test
 power = sampsizepwr ("t", [20, 5], 25, [], 5, "Tail", "right");
 assert (power, 0.5797373588621888, 1e-14);
***** test
 nout = sampsizepwr ("t", [20, 5], 25, 0.99, [], "Tail", "right");
 assert (nout, 18);
***** test
 p1out = sampsizepwr ("t", [20, 5], [], 0.95, 10, "Tail", "right");
 assert (p1out, 25.65317979360237, 1e-14);
***** test
 pwr = sampsizepwr ("t2", [1.4, 0.2], 1.7, [], 5, "Ratio", 2);
 assert (pwr, 0.716504004686586, 1e-14);
***** test
 n = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, []);
 assert (n, 11);
***** test
 [n1, n2] = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, [], "Ratio", 2);
 assert ([n1, n2], [8, 16]);
68 tests, 68 passed, 0 known failure, 0 skipped
[inst/wblplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/wblplot.m
***** demo
 x = [16 34 53 75 93 120];
 wblplot (x);
***** demo
 x = [2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67]';
 c = [0 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1]';
 [h, p] = wblplot (x, c);
 p
***** demo
 x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
 [h, p] = wblplot (x, [], [], 0.05);
 p
 ## Benchmark Reliasoft eta = 146.2545 beta 1.1973 rho = 0.9999
***** demo
 x = [46 64 83 105 123 150 150];
 c = [0 0 0 0 0 0 1];
 f = [1 1 1 1 1 1 4];
 wblplot (x, c, f, 0.05);
***** demo
 x = [46 64 83 105 123 150 150];
 c = [0 0 0 0 0 0 1];
 f = [1 1 1 1 1 1 4];
 ## Subtract 30.92 from x to simulate a 3 parameter wbl with gamma = 30.92
 wblplot (x - 30.92, c, f, 0.05);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
   [h, p] = wblplot (x, [], [], 0.05);
   assert (numel (h), 4)
   assert (p(1), 146.2545, 1E-4)
   assert (p(2), 1.1973, 1E-4)
   assert (p(3), 0.9999, 5E-5)
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
1 test, 1 passed, 0 known failure, 0 skipped
[inst/pcares.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pcares.m
***** demo
 x = [ 7    26     6    60;
       1    29    15    52;
      11    56     8    20;
      11    31     8    47;
       7    52     6    33;
      11    55     9    22;
       3    71    17     6;
       1    31    22    44;
       2    54    18    22;
      21    47     4    26;
       1    40    23    34;
      11    66     9    12;
      10    68     8    12];

 ## As we increase the number of principal components, the norm
 ## of the residuals matrix will decrease
 r1 = pcares (x,1);
 n1 = norm (r1)
 r2 = pcares (x,2);
 n2 = norm (r2)
 r3 = pcares (x,3);
 n3 = norm (r3)
 r4 = pcares (x,4);
 n4 = norm (r4)
***** test
 load hald
 r1 = pcares (ingredients,1);
 r2 = pcares (ingredients,2);
 r3 = pcares (ingredients,3);
 assert (r1(1,:), [2.0350,  2.8304, -6.8378, 3.0879], 1e-4);
 assert (r2(1,:), [-2.4037, 2.6930, -1.6482, 2.3425], 1e-4);
 assert (r3(1,:), [ 0.2008, 0.1957,  0.2045, 0.1921], 1e-4);
***** error<pcares: too few input arguments.> pcares (ones (20, 3))
***** error<pcares: NDIM must be less than or equal to the column of X.> ...
 pcares (ones (30, 2), 3)
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/gmdistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/gmdistribution.m
***** test
 mu = eye(2);
 Sigma = eye(2);
 GM = gmdistribution (mu, Sigma);
 density = GM.pdf ([0 0; 1 1]);
 assert (density(1) - density(2), 0, 1e-6);

 [idx, nlogl, P, logpdf,M] = cluster (GM, eye(2));
 assert (idx, [1; 2]);
 [idx2,nlogl2,P2,logpdf2] = GM.cluster (eye(2));
 assert (nlogl - nlogl2, 0, 1e-6);
 [idx3,nlogl3,P3] = cluster (GM, eye(2));
 assert (P - P3, zeros (2), 1e-6);
 [idx4,nlogl4] = cluster (GM, eye(2));
 assert (size (nlogl4), [1 1]);
 idx5 = cluster (GM, eye(2));
 assert (idx - idx5, zeros (2,1));

 D = GM.mahal ([1;0]);
 assert (D - M(1,:), zeros (1,2), 1e-6);

 P = GM.posterior ([0 1]);
 assert (P - P2(2,:), zeros (1,2), 1e-6);

 R = GM.random(20);
 assert (size(R), [20, 2]);

 R = GM.random();
 assert (size(R), [1, 2]);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/vartest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/vartest.m
***** error<vartest: too few input arguments.> vartest ();
***** error<vartest: invalid value for variance.> vartest ([1, 2, 3, 4], -0.5);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 1.2);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", "val");
***** error<vartest: invalid value for tail.>  ...
 vartest ([1, 2, 3, 4], 1, "tail", "val");
***** error<vartest: invalid value for tail.>  ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "val");
***** error<vartest: invalid value for operating dimension.> ...
 vartest ([1, 2, 3, 4], 1, "dim", 3);
***** error<vartest: invalid value for operating dimension.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "dim", 3);
***** error<vartest: invalid name for optional arguments.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "badoption", 3);
***** error<vartest: optional arguments must be in name/value pairs.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail");
***** test
 load carsmall
 [h, pval, ci] = vartest (MPG, 7^2);
 assert (h, 1);
 assert (pval, 0.04335086742174443, 1e-14);
 assert (ci, [49.397; 88.039], 1e-3);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/grp2idx.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/grp2idx.m
***** test
 in = [true false false true];
 out = {[1; 2; 2; 1] {"1"; "0"} [true; false]};
 assert (nthargout (1:3, @grp2idx, in), out)
 assert (nthargout (1:3, @grp2idx, in), nthargout (1:3, @grp2idx, in'))
***** test
 assert (nthargout (1:3, @grp2idx, [false, true]),
         {[1; 2] {"0"; "1"} [false; true]});
 assert (nthargout (1:3, @grp2idx, [true, false]),
         {[1; 2] {"1"; "0"} [true; false]});
***** assert (nthargout (1:3, @grp2idx, ["oct"; "sci"; "oct"; "oct"; "sci"]),
        {[1; 2; 1; 1; 2] {"oct"; "sci"} ["oct"; "sci"]});
***** assert (nthargout (1:3, @grp2idx, {"oct"; "sci"; "oct"; "oct"; "sci"}),
        {[1; 2; 1; 1; 2] {"oct"; "sci"} {"oct"; "sci"}});
***** assert (nthargout (1:3, @grp2idx, [ 1 -3 -2 -3 -3  2  1 -1  3 -3]),
        {[1; 2; 3; 2; 2; 4; 1; 5; 6; 2], {"1"; "-3"; "-2"; "2"; "-1"; "3"}, ...
         [1; -3; -2; 2; -1; 3]});
***** assert (nthargout (1:3, @grp2idx, [2 2 3 NaN 2 3]),
        {[1; 1; 2; NaN; 1; 2] {"2"; "3"} [2; 3]})
***** assert (nthargout (1:3, @grp2idx, {"et" "sa" "sa" "" "et"}),
        {[1; 2; 2; NaN; 1] {"et"; "sa"} {"et"; "sa"}})
***** test assert (nthargout (1:3, @grp2idx, ["sci"; "oct"; "sci"; "oct"; "oct"]),
        {[1; 2; 1; 2; 2] {"sci"; "oct"} ["sci"; "oct"]});
***** test assert (nthargout (1:3, @grp2idx, {"sci"; "oct"; "sci"; "oct"; "oct"}),
        {[1; 2; 1; 2; 2] {"sci"; "oct"} {"sci"; "oct"}});
***** test assert (nthargout (1:3, @grp2idx, {"sa" "et" "et" "" "sa"}),
        {[1; 2; 2; NaN; 1] {"sa"; "et"} {"sa"; "et"}})
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/histfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/histfit.m
***** demo
 histfit (randn (100, 1))
***** demo
 histfit (poissrnd (2, 1000, 1), 10, "Poisson")
***** demo
 histfit (betarnd (3, 10, 1000, 1), 10, "beta")
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x, 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (randn (100, 1));
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (poissrnd (2, 1000, 1), 10, "Poisson");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (betarnd (3, 10, 1000, 1), 10, "beta");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, randn (100, 1));
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, poissrnd (2, 1000, 1), 10, "Poisson");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, betarnd (3, 10, 1000, 1), 10, "beta");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = axes ("parent", hf);
   fail ("histfit (ax)", "histfit: too few input arguments.");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error<histfit: X must be a numeric vector of real numbers.> ...
 histfit ('wer')
***** error<histfit: no data in X.> histfit ([NaN, NaN, NaN]);
***** error<histfit: NBINS must be a real scalar integer value.> ...
 histfit (randn (100, 1), 5.6)
***** error<histfit: DISTNAME must be a character vector.> ...
 histfit (randn (100, 1), 8, 5)
***** error<histfit: DISTNAME must be a character vector.> ...
 histfit (randn (100, 1), 8, {'normal'})
***** error<histfit: 'Kernel' distribution is not supported yet.> ...
 histfit (randn (100, 1), 8, 'Kernel')
***** error<histfit: unrecognized distribution name.> ...
 histfit (randn (100, 1), 8, 'ASDASDASD')
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/crossval.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/crossval.m
***** test
 load fisheriris
 y = meas(:, 1);
 X = [ones(size(y)) meas(:, 2:4)];
 f = @(X1, y1, X2, y2) meansq (y2 - X2*regress(y1, X1));
 results0 = crossval (f, X, y);
 results1 = crossval (f, X, y, 'KFold', 10);
 folds = 5;
 results2 = crossval (f, X, y, 'KFold', folds);
 results3 = crossval (f, X, y, 'Partition', cvpartition (numel (y), 'KFold', folds));
 results4 = crossval (f, X, y, 'LeaveOut', 1);
 mcreps = 2; n_holdout = 20;
 results5 = crossval (f, X, y, 'HoldOut', n_holdout, 'mcreps', mcreps);

 ## ensure equal representation of iris species in the training set -- tends
 ## to slightly reduce cross-validation mean square error
 results6 = crossval (f, X, y, 'KFold', 5, 'stratify', grp2idx(species));

 assert (results0, results1, 2e-15);
 assert (results2, results3, 5e-17);
 assert (size(results4), [1 numel(y)]);
 assert (mean(results4), 0.1018, 1e-4);
 assert (size(results5), [mcreps 1]);
warning: strmatch is obsolete; use strncmp or strcmp instead
1 test, 1 passed, 0 known failure, 0 skipped
[inst/correlation_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/correlation_test.m
***** error<Invalid call to correlation_test.  Correct usage> correlation_test ();
***** error<Invalid call to correlation_test.  Correct usage> correlation_test (1);
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 NaN]', [2 3 4]');
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 Inf]', [2 3 4]');
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 3+i]', [2 3 4]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [2 3 NaN]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [2 3 Inf]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [3 4 3+i]');
***** error<correlation_test: X and Y must be vectors of equal length.> ...
 correlation_test ([1 2 3]', [3 4 4 5]');
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 0);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 1.2);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", "a");
***** error<correlation_test: invalid Name argument.> ...
 correlation_test ([1 2 3]', [2 3 4]', "some", 0.05);
***** error<correlation_test: invalid value for tail.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "tail", "val");
***** error<correlation_test: invalid value for tail.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
***** error<correlation_test: invalid value for method.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "method", 0.01);
***** error<correlation_test: invalid value for method.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "method", "some");
***** test
 x = [6 7 7 9 10 12 13 14 15 17];
 y = [19 22 27 25 30 28 30 29 25 32];
 [h, pval, stats] = correlation_test (x, y);
 assert (stats.corrcoef, corr (x', y'), 1e-14);
 assert (pval, 0.0223, 1e-4);
***** test
 x = [6 7 7 9 10 12 13 14 15 17]';
 y = [19 22 27 25 30 28 30 29 25 32]';
 [h, pval, stats] = correlation_test (x, y);
 assert (stats.corrcoef, corr (x, y), 1e-14);
 assert (pval, 0.0223, 1e-4);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/stepwisefit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/stepwisefit.m
***** test
 % Sample data from Draper and Smith (n = 13, k = 4)
 X = [7 1 11 11 7 11 3 1 2 21 1 11 10; ...
     26 29 56 31 52 55 71 31 54 47 40 66 68; ...
     6 15 8 8 6 9 17 22 18 4 23 9 8; ...
     60 52 20 47 33 22 6 44 22 26 34 12 12]';
 y = [78.5 74.3 104.3 87.6 95.9 109.2 102.7 72.5 93.1 115.9 83.8 113.3 109.4]';
 [X_use, b, bint, r, rint, stats] = stepwisefit(y, X);
 assert(X_use, [4 1])
 assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
 [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, 0.05, 0.1, "corr");
 assert(X_use, [4 1])
 assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
 [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, [], [], "p");
 assert(X_use, [4 1])
 assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
1 test, 1 passed, 0 known failure, 0 skipped
[inst/loadmodel.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/loadmodel.m
***** error<loadmodel: too few arguments.> loadmodel ()
***** error<loadmodel: 'fisheriris.mat' does not contain a Classification or Regression object.> ...
 loadmodel ("fisheriris.mat")
***** error<loadmodel: 'ClassificationModel' is not supported.> ...
 loadmodel ("fail_loadmodel.mdl")
***** error<ClassificationKNN.load_model: invalid model in 'fail_load_model.mdl'.> ...
 loadmodel ("fail_load_model.mdl")
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/ttest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ttest2.m
***** test
 a = 1:5;
 b = 6:10;
 b(5) = NaN;
 [h,p,ci,stats] = ttest2 (a,b);
 assert (h, 1);
 assert (p, 0.002535996080258229, 1e-14);
 assert (ci, [-6.822014919225481, -2.17798508077452], 1e-14);
 assert (stats.tstat, -4.582575694955839, 1e-14);
 assert (stats.df, 7);
 assert (stats.sd, 1.4638501094228, 1e-13);
***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", "invalid");
***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", 25);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/ppplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ppplot.m
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ppplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error ppplot ()
***** error <ppplot: X must be a numeric vector> ppplot (ones (2,2))
***** error <ppplot: X must be a numeric vector> ppplot (1, 2)
***** error <ppplot: DIST must be a strin> ppplot ([1 2 3 4], 2)
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/inconsistent.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/inconsistent.m
***** error inconsistent ()
***** error inconsistent ([1 2 1], 2, 3)
***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 2))
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], -1)
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], 1.3)
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], [1 1])
***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 3))
***** test
 load fisheriris;
 Z = linkage(meas, 'average', 'chebychev');
 assert (cond (inconsistent (Z)), 39.9, 1e-3);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/rmmissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/rmmissing.m
***** assert (rmmissing ([1,NaN,3]), [1,3])
***** assert (rmmissing ('abcd f'), 'abcdf')
***** assert (rmmissing ({'xxx','','xyz'}), {'xxx','xyz'})
***** assert (rmmissing ({'xxx','';'xyz','yyy'}), {'xyz','yyy'})
***** assert (rmmissing ({'xxx','';'xyz','yyy'}, 2), {'xxx';'xyz'})
***** assert (rmmissing ([1,2;NaN,2]), [1,2])
***** assert (rmmissing ([1,2;NaN,2], 2), [2,2]')
***** assert (rmmissing ([1,2;NaN,4;NaN,NaN],"MinNumMissing", 2), [1,2;NaN,4])
***** test
 x = [1:6];
 x([2,4]) = NaN;
 [~, idx] = rmmissing (x);
 assert (idx, logical ([0, 1, 0, 1, 0, 0]));
 assert (class(idx), 'logical');
 x = reshape (x, [2, 3]);
 [~, idx] = rmmissing (x);
 assert (idx, logical ([0; 1]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 2);
 assert (idx, logical ([1, 1, 0]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 1, "MinNumMissing", 2);
 assert (idx, logical ([0; 1]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 2, "MinNumMissing", 2);
 assert (idx, logical ([0, 0, 0]));
 assert (class(idx), 'logical');
***** assert (rmmissing (single ([1 2 NaN; 3 4 5])), single ([3 4 5]))
***** assert (rmmissing (logical (ones (3))), logical (ones (3)))
***** assert (rmmissing (int32 (ones (3))), int32 (ones (3)))
***** assert (rmmissing (uint32 (ones (3))), uint32 (ones (3)))
***** assert (rmmissing ({1, 2, 3}), {1, 2, 3})
***** assert (rmmissing ([struct, struct, struct]), [struct, struct, struct])
***** assert (rmmissing ([]), [])
***** assert (rmmissing (ones (1,0)), ones (1,0))
***** assert (rmmissing (ones (1,0), 1), ones (1,0))
***** assert (rmmissing (ones (1,0), 2), ones (1,0))
***** assert (rmmissing (ones (0,1)), ones (0,1))
***** assert (rmmissing (ones (0,1), 1), ones (0,1))
***** assert (rmmissing (ones (0,1), 2), ones (0,1))
***** error <input dimension> rmmissing (ones (0,1,2))
***** error rmmissing ()
***** error <input dimension> rmmissing (ones(2,2,2))
***** error <must be either 1 or 2> rmmissing ([1 2; 3 4], 5)
***** error <unknown parameter name> rmmissing ([1 2; 3 4], "XXX", 1)
***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], 2, "MinNumMissing", -2)
***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 3.8)
***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", [1 2 3])
***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 'xxx')
31 tests, 31 passed, 0 known failure, 0 skipped
Checking C++ files ...
[src/svmtrain.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/svmtrain.cc
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain(L, D, '-c 1 -g 0.07');
 [predict_label, accuracy, dec_values] = svmpredict(L, D, model);
 assert (isstruct (model), true);
 assert (isfield (model, "Parameters"), true);
 assert (model.totalSV, 130);
 assert (model.nr_class, 2);
 assert (size (model.Label), [2, 1]);
***** shared L, D
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
***** error <svmtrain: wrong number of output arguments.> [L, D] = svmtrain (L, D);
***** error <svmtrain: label vector and instance matrix must be double.> ...
 model = svmtrain (single (L), D);
***** error <svmtrain: wrong number of input arguments.> ...
 model = svmtrain (L, D, "", "");
4 tests, 4 passed, 0 known failure, 0 skipped
[src/fcnnpredict.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/fcnnpredict.cc
***** shared X, Y, MODEL
 load fisheriris
 X = meas;
 Y = grp2idx (species);
 MODEL = fcnntrain (X, Y, 10, [1, 1], 0.025, 100, false);
***** test
 [Y_pred, Y_scores] = fcnnpredict (MODEL, X);
 assert (numel (Y_pred), numel (Y));
 assert (isequal (size (Y_pred), size (Y)), true);
 assert (columns (Y_scores), numel (unique (Y)));
 assert (rows (Y_scores), numel (Y));
***** error <fcnnpredict: too few input arguments.> ...
 fcnnpredict (MODEL);
***** error <fcnnpredict: too many output arguments.> ...
 [Q, W, E] = fcnnpredict (MODEL, X);
***** error <fcnnpredict: first argument must be a scalar structure.> ...
 fcnnpredict (1, X);
***** error <fcnnpredict: first argument must be a scalar structure.> ...
 fcnnpredict (struct ("L", {1, 2, 3}), X);
***** error <fcnnpredict: model does not have a 'LayerWeights' field.> ...
 fcnnpredict (struct ("L", 1), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", 1), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", {1}), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", {{1; 2; 3}}), X);
***** error <fcnnpredict: model does not have an 'Activations' field.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, "R", 2), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", [2]), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", [2; 2]), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", {{2, 2}}), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", {{"sigmoid", "softmax"}}), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", "sigmoid"), X);
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, complex (X));
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, {1, 2, 3, 4});
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, "asd");
***** error <fcnnpredict: XC cannot be empty.> ...
 fcnnpredict (MODEL, []);
***** error <fcnnpredict: the features in XC do not match the trained model.> ...
 fcnnpredict (MODEL, X(:,[1:3]));
20 tests, 20 passed, 0 known failure, 0 skipped
[src/editDistance.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/editDistance.cc
***** error <editDistance: too many input arguments.> d = editDistance (1, 2, 3, 4);
***** error <editDistance: too many output arguments.> ...
 [C, IA, IC, I] = editDistance ({"AS","SD","AD"}, 1);
***** error <editDistance: too many output arguments.> ...
 [C, IA] = editDistance ({"AS","SD","AD"});
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ({"AS","SD","AD"}, [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, 1.25);
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, 1.25);
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ("string1", "string2", [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ("string1", "string2", -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ("string1", "string2", 1.25);
***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
 d = editDistance ({{"string1", "string2"}, 2});
***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
 d = editDistance ({{"string1", "string2"}, 2}, 2);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance ([1, 2, 3]);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance (["AS","SD","AD","AS"]);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance (["AS","SD","AD"], 2);
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance (logical ([1,2,3]), {"AS","AS","AD"});
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance ({"AS","SD","AD"}, logical ([1,2,3]));
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance ([1,2,3], {"AS","AS","AD"});
***** error <editDistance: first tokenizedDocument does not contain cellstr arrays.> ...
 d = editDistance ({1,2,3}, {"AS","SD","AD"});
***** error <editDistance: second tokenizedDocument does not contain cellstr arrays.> ...
 d = editDistance ({"AS","SD","AD"}, {1,2,3});
***** error <editDistance: cellstr input arguments size mismatch.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS", "AS"});
***** test
 d = editDistance ({"AS","SD","AD"});
 assert (d, [2; 1; 1]);
 assert (class (d), "double");
***** test
 C = editDistance ({"AS","SD","AD"}, 1);
 assert (iscellstr (C), true);
 assert (C, {"AS";"SD"});
***** test
 [C, IA] = editDistance ({"AS","SD","AD"}, 1);
 assert (class (IA), "double");
 assert (IA, [1;2]);
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA] = editDistance (A, 2, "OutputAllIndices", false);
 assert (class (IA), "double");
 assert (A(IA), C);
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA] = editDistance (A, 2, "OutputAllIndices", true);
 assert (class (IA), "cell");
 assert (C, {"ASS"; "FDE"; "OPA"});
 assert (A(IA{1}), {"ASS"; "SDS"; "EDS"});
 assert (A(IA{2}), {"FDE"; "EDS"});
 assert (A(IA{3}), {"OPA"});
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA, IC] = editDistance (A, 2);
 assert (class (IA), "double");
 assert (A(IA), C);
 assert (IC, [1; 1; 3; 1; 5]);
***** test
 d = editDistance ({"AS","SD","AD"}, {"AS", "AD", "SE"});
 assert (d, [0; 1; 2]);
 assert (class (d), "double");
***** test
 d = editDistance ({"AS","SD","AD"}, {"AS"});
 assert (d, [0; 2; 1]);
 assert (class (d), "double");
***** test
 d = editDistance ({"AS"}, {"AS","SD","AD"});
 assert (d, [0; 2; 1]);
 assert (class (d), "double");
***** test
 b = editDistance ("Octave", "octave");
 assert (b, 1);
 assert (class (b), "double");
33 tests, 33 passed, 0 known failure, 0 skipped
[src/libsvmread.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/libsvmread.cc
***** error <libsvmread: filename must be a string.> [L, D] = libsvmread (24);
***** error <libsvmread: wrong number of input or output arguments.> ...
 D = libsvmread ("filename");
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 assert (size (L), [270, 1]);
 assert (size (D), [270, 13]);
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 assert (issparse (L), false);
 assert (issparse (D), true);
4 tests, 4 passed, 0 known failure, 0 skipped
[src/svmpredict.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/svmpredict.cc
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain (L, D, '-c 1 -g 0.07');
 [predict_label, accuracy, dec_values] = svmpredict (L, D, model);
 assert (size (predict_label), size (dec_values));
 assert (accuracy, [86.666, 0.533, 0.533]', [1e-3, 1e-3, 1e-3]');
 assert (dec_values(1), 1.225836001973273, 1e-14);
 assert (dec_values(2), -0.3212992933043805, 1e-14);
 assert (predict_label(1), 1);
***** shared L, D, model
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain (L, D, '-c 1 -g 0.07');
***** error <svmpredict: wrong number of output arguments.> ...
 [p, a] = svmpredict (L, D, model);
***** error <svmpredict: wrong number of input arguments.> p = svmpredict (L, D);
***** error <svmpredict: label vector and instance matrix must be double.> ...
 p = svmpredict (single (L), D, model);
***** error <svmpredict: model should be a struct array.> p = svmpredict (L, D, 123);
5 tests, 5 passed, 0 known failure, 0 skipped
[src/libsvmwrite.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/libsvmwrite.cc
***** shared L, D
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
***** error <libsvmwrite: error opening file for write.> libsvmwrite ("", L, D);
***** error <libsvmwrite: length of label vector does not match instances.> ...
 libsvmwrite (tempname (), [L;L], D);
***** error <libsvmwrite: wrong number of output arguments.> ...
 OUT = libsvmwrite (tempname (), L, D);
***** error <libsvmwrite: label vector and instance matrix must be double.> ...
 libsvmwrite (tempname (), single (L), D);
***** error <libsvmwrite: filename must be a string.> libsvmwrite (13412, L, D);
***** error <libsvmwrite: instance_matrix must be sparse.> ...
 libsvmwrite (tempname (), L, full (D));
***** error <libsvmwrite: wrong number of input arguments.> ...
 libsvmwrite (tempname (), L, D, D);
7 tests, 7 passed, 0 known failure, 0 skipped
[src/fcnntrain.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/fcnntrain.cc
***** shared X, Y, MODEL
 load fisheriris
 X = meas;
 Y = grp2idx (species);
***** error <fcnntrain: too few input arguments.> ...
 model = fcnntrain (X, Y);
***** error <fcnntrain: too many output arguments.> ...
 [Q, W] = fcnntrain (X, Y, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: X must be a real numeric matrix.> ...
 fcnntrain (complex (X), Y, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: X must be a real numeric matrix.> ...
 fcnntrain ({X}, Y, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: X cannot be empty.> ...
 fcnntrain ([], Y, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: Y must be a real numeric matrix.> ...
 fcnntrain (X, complex (Y), 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: Y must be a real numeric matrix.> ...
 fcnntrain (X, {Y}, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: Y cannot be empty.> ...
 fcnntrain (X, [], 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: X and Y must have the same number of rows.> ...
 fcnntrain (X, Y([1:50]), 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: labels in Y must be positive integers.> ...
 fcnntrain (X, Y - 1, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: LayerSizes must be a row vector of integer values.> ...
 fcnntrain (X, Y, [10; 5], [1, 1, 1], 0.025, 50, false);
***** error <fcnntrain: LayerSizes must be a row vector of integer values.> ...
 fcnntrain (X, Y, "10", [1, 1], 0.025, 50, false);
***** error <fcnntrain: LayerSizes must be a row vector of integer values.> ...
 fcnntrain (X, Y, {10}, [1, 1], 0.025, 50, false);
***** error <fcnntrain: LayerSizes must be a row vector of integer values.> ...
 fcnntrain (X, Y, complex (10), [1, 1], 0.025, 50, false);
***** error <fcnntrain: Activations must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, [1; 1], 0.025, 50, false);
***** error <fcnntrain: Activations must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, {1, 1}, 0.025, 50, false);
***** error <fcnntrain: Activations must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, "1", 0.025, 50, false);
***** error <fcnntrain: Activations must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, complex ([1, 1]), 0.025, 50, false);
***** error <fcnntrain: Activations do not match LayerSizes.> ...
 fcnntrain (X, Y, 10, [1, 1, 1], 0.025, 50, false);
***** error <fcnntrain: cannot have a layer of zero size.> ...
 fcnntrain (X, Y, [10, 0, 5], [1, 1, 1, 1], 0.025, 50, false);
***** error <fcnntrain: invalid Activations code.> ...
 fcnntrain (X, Y, 10, [-1, 1], 0.025, 50, false);
***** error <fcnntrain: invalid Activations code.> ...
 fcnntrain (X, Y, 10, [8, 1], 0.025, 50, false);
***** error <fcnntrain: LearningRate must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], -0.025, 50, false);
***** error <fcnntrain: LearningRate must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 0, 50, false);
***** error <fcnntrain: LearningRate must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], [0.025, 0.001], 50, false);
***** error <fcnntrain: LearningRate must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], {0.025}, 50, false);
***** error <fcnntrain: Epochs must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 0.025, 0, false);
***** error <fcnntrain: Epochs must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 0.025, [50, 25], false);
***** error <fcnntrain: DisplayInfo must be a boolean scalar.> ...
 fcnntrain (X, Y, 10, [1, 1], 0.025, 50, 0);
***** error <fcnntrain: DisplayInfo must be a boolean scalar.> ...
 fcnntrain (X, Y, 10, [1, 1], 0.025, 50, 1);
30 tests, 30 passed, 0 known failure, 0 skipped
Done running the unit tests.
Summary: 11048 tests, 11045 passed, 1 known failures, 2 skipped
   dh_install -O--buildsystem=octave
   dh_installdocs -O--buildsystem=octave
   dh_installchangelogs -O--buildsystem=octave
   dh_octave_changelogs -O--buildsystem=octave
   dh_octave_examples -O--buildsystem=octave
   dh_installsystemduser -O--buildsystem=octave
   dh_perl -O--buildsystem=octave
   dh_link -O--buildsystem=octave
   dh_strip_nondeterminism -O--buildsystem=octave
   dh_compress -O--buildsystem=octave
   dh_fixperms -O--buildsystem=octave
   dh_missing -O--buildsystem=octave
   dh_dwz -a -O--buildsystem=octave
   dh_strip -a -O--buildsystem=octave
   dh_makeshlibs -a -O--buildsystem=octave
   dh_shlibdeps -a -O--buildsystem=octave
dpkg-shlibdeps: warning: diversions involved - output may be incorrect
 diversion by libc6 from: /lib/ld-linux-armhf.so.3
dpkg-shlibdeps: warning: diversions involved - output may be incorrect
 diversion by libc6 to: /lib/ld-linux-armhf.so.3.usr-is-merged
   dh_octave_substvar -O--buildsystem=octave
   debian/rules execute_before_dh_installdeb-indep
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.7.0'
# Remove empty directory
rm -r $(find debian/octave-statistics-common -name doc -type d -empty)
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.7.0'
   dh_installdeb -O--buildsystem=octave
   dh_gencontrol -O--buildsystem=octave
dpkg-gencontrol: warning: package octave-statistics-common: substitution variable ${octave:Upstream-Description} unused, but is defined
dpkg-gencontrol: warning: package octave-statistics: substitution variable ${octave:Upstream-Description} unused, but is defined
dpkg-gencontrol: warning: package octave-statistics: substitution variable ${octave:Upstream-Description} unused, but is defined
   dh_md5sums -O--buildsystem=octave
   dh_builddeb -O--buildsystem=octave
dpkg-deb: building package 'octave-statistics-dbgsym' in '../octave-statistics-dbgsym_1.7.0-2_armhf.deb'.
dpkg-deb: building package 'octave-statistics' in '../octave-statistics_1.7.0-2_armhf.deb'.
dpkg-deb: building package 'octave-statistics-common' in '../octave-statistics-common_1.7.0-2_all.deb'.
 dpkg-genbuildinfo --build=binary -O../octave-statistics_1.7.0-2_armhf.buildinfo
 dpkg-genchanges --build=binary -O../octave-statistics_1.7.0-2_armhf.changes
dpkg-genchanges: info: binary-only upload (no source code included)
 dpkg-source --after-build .
dpkg-buildpackage: info: binary-only upload (no source included)
dpkg-genchanges: info: not including original source code in upload
I: copying local configuration
I: unmounting dev/ptmx filesystem
I: unmounting dev/pts filesystem
I: unmounting dev/shm filesystem
I: unmounting proc filesystem
I: unmounting sys filesystem
I: cleaning the build env 
I: removing directory /srv/workspace/pbuilder/30583 and its subdirectories
I: Current time: Thu Oct 17 22:06:31 -12 2024
I: pbuilder-time-stamp: 1729245991
Fri Oct 18 10:06:58 UTC 2024  I: 1st build successful. Starting 2nd build on remote node virt32c-armhf-rb.debian.net.
Fri Oct 18 10:06:58 UTC 2024  I: Preparing to do remote build '2' on virt32c-armhf-rb.debian.net.
Fri Oct 18 10:44:15 UTC 2024  I: Deleting $TMPDIR on virt32c-armhf-rb.debian.net.
Fri Oct 18 10:44:17 UTC 2024  I: octave-statistics_1.7.0-2_armhf.changes:
Format: 1.8
Date: Mon, 16 Sep 2024 18:55:03 +0200
Source: octave-statistics
Binary: octave-statistics octave-statistics-common octave-statistics-dbgsym
Architecture: all armhf
Version: 1.7.0-2
Distribution: unstable
Urgency: medium
Maintainer: Debian Octave Group <team+pkg-octave-team@tracker.debian.org>
Changed-By: Sébastien Villemot <sebastien@debian.org>
Description:
 octave-statistics - additional statistical functions for Octave
 octave-statistics-common - additional statistical functions for Octave (arch-indep files)
Closes: 1081727
Changes:
 octave-statistics (1.7.0-2) unstable; urgency=medium
 .
   * test-ClassificationPartitionedModel.patch: new patch.
     Disables tests that randomly enters an infinite loop. (Closes: #1081727)
Checksums-Sha1:
 67eb603a2797fa364888b68acfb68661223548ed 872160 octave-statistics-common_1.7.0-2_all.deb
 37d0a33d2a5f092fa981c20c4f3725d05924f83a 3317552 octave-statistics-dbgsym_1.7.0-2_armhf.deb
 d53333402058b01a61d847ef4dbfa4d95e06469b 22046 octave-statistics_1.7.0-2_armhf.buildinfo
 2973d3e24ff1a305644d50805b8e94cc1f5fc83b 118500 octave-statistics_1.7.0-2_armhf.deb
Checksums-Sha256:
 ec2f625cb35d17d862147fb3f821206809b4b0e0bc4b0ea35728ab9cbff6d73a 872160 octave-statistics-common_1.7.0-2_all.deb
 57c7b31e40e95f65a24fb454cfa1858f9304f8e9a9a6ace265b871fdd3640d53 3317552 octave-statistics-dbgsym_1.7.0-2_armhf.deb
 866cc57e8fa2e6c97796d8501d5ed19846aa7a16701b3d5e823f533910d13941 22046 octave-statistics_1.7.0-2_armhf.buildinfo
 4ee7b02b379bdd0fa8fa45facf6f1fe192583649659c5267a4076937fbc539d6 118500 octave-statistics_1.7.0-2_armhf.deb
Files:
 2f03ac4eab8d548fd83a5a72dc6e60c6 872160 math optional octave-statistics-common_1.7.0-2_all.deb
 859751eacfc468c88b75f3dccd326463 3317552 debug optional octave-statistics-dbgsym_1.7.0-2_armhf.deb
 12b0d486c19fdfed42f4ab60885fd760 22046 math optional octave-statistics_1.7.0-2_armhf.buildinfo
 cc91a7a1441c8095929c85509841ad93 118500 math optional octave-statistics_1.7.0-2_armhf.deb
Fri Oct 18 10:44:21 UTC 2024  I: diffoscope 280 will be used to compare the two builds:
Running as unit: rb-diffoscope-armhf_8-10670.service
Warning: program compiled against libxml 212 using older 209
# Profiling output for: /usr/bin/diffoscope --timeout 7200 --html /srv/reproducible-results/rbuild-debian/r-b-build.KZf1uxCT/octave-statistics_1.7.0-2.diffoscope.html --text /srv/reproducible-results/rbuild-debian/r-b-build.KZf1uxCT/octave-statistics_1.7.0-2.diffoscope.txt --json /srv/reproducible-results/rbuild-debian/r-b-build.KZf1uxCT/octave-statistics_1.7.0-2.diffoscope.json --profile=- /srv/reproducible-results/rbuild-debian/r-b-build.KZf1uxCT/b1/octave-statistics_1.7.0-2_armhf.changes /srv/reproducible-results/rbuild-debian/r-b-build.KZf1uxCT/b2/octave-statistics_1.7.0-2_armhf.changes

## command (total time: 0.000s)
       0.000s      1 call     cmp (internal)

## has_same_content_as (total time: 0.000s)
       0.000s      1 call     abc.DotChangesFile

## main (total time: 0.536s)
       0.536s      2 calls    outputs
       0.000s      1 call     cleanup

## recognizes (total time: 0.071s)
       0.071s     12 calls    diffoscope.comparators.binary.FilesystemFile

## specialize (total time: 0.000s)
       0.000s      1 call     specialize
Finished with result: success
Main processes terminated with: code=exited/status=0
Service runtime: 971ms
CPU time consumed: 957ms
Fri Oct 18 10:44:22 UTC 2024  I: diffoscope 280 found no differences in the changes files, and a .buildinfo file also exists.
Fri Oct 18 10:44:22 UTC 2024  I: octave-statistics from trixie built successfully and reproducibly on armhf.
Fri Oct 18 10:44:24 UTC 2024  I: Submitting .buildinfo files to external archives:
Fri Oct 18 10:44:24 UTC 2024  I: Submitting 24K	b1/octave-statistics_1.7.0-2_armhf.buildinfo.asc
Fri Oct 18 10:44:25 UTC 2024  I: Submitting 24K	b2/octave-statistics_1.7.0-2_armhf.buildinfo.asc
Fri Oct 18 10:44:26 UTC 2024  I: Done submitting .buildinfo files to http://buildinfo.debian.net/api/submit.
Fri Oct 18 10:44:26 UTC 2024  I: Done submitting .buildinfo files.
Fri Oct 18 10:44:26 UTC 2024  I: Removing signed octave-statistics_1.7.0-2_armhf.buildinfo.asc files:
removed './b1/octave-statistics_1.7.0-2_armhf.buildinfo.asc'
removed './b2/octave-statistics_1.7.0-2_armhf.buildinfo.asc'